User recognition for speech processing systems

ABSTRACT

Systems, methods, and devices for recognizing a user are disclosed. A speech-controlled device captures a spoken utterance, and sends audio data corresponding thereto to a server. The server determines content sources storing or having access to content responsive to the spoken utterance. The server also determines multiple users associated with a profile of the speech-controlled device. Using the audio data, the server may determine user recognition data with respect to each user indicated in the speech-controlled device&#39;s profile. The server may also receive user recognition confidence threshold data from each of the content sources. The server may determine user recognition data associated that satisfies (i.e., meets or exceeds) a most stringent (i.e., highest) of the user recognition confidence threshold data. Thereafter, the server may send data indicating a user associated with the user recognition data to all of the content sources.

CROSS REFERENCE TO RELATED APPLICATION

This application is a continuation of, and claims the benefit ofpriority of, U.S. Non-provisional patent application Ser. No.16/020,603, filed Jun. 27, 2018 and entitled “USER RECOGNITION FORSPEECH PROCESSING SYSTEMS”, which is a continuation of, and claims thebenefit of priority of, U.S. Non-provisional patent application Ser. No.15/385,138, filed Dec. 20, 2016 and entitled “USER RECOGNITION FORSPEECH PROCESSING SYSTEMS,” in the names of Natalia Vladimirovna Mamkinaet al., which issued as U.S. Pat. No. 10,032,451, each of which isherein incorporated by reference in its entirety.

BACKGROUND

Speech recognition systems have progressed to the point where humans caninteract with computing devices by speaking. Such systems employtechniques to identify the words spoken by a human user based on thevarious qualities of a received audio input. Speech recognition combinedwith natural language understanding processing techniques enablespeech-based user control of a computing device to perform tasks basedon the user's spoken commands. The combination of speech recognition andnatural language understanding processing techniques is referred toherein as speech processing. Speech processing may also involveconverting a user's speech into text data which may then be provided tovarious text-based software applications.

Speech processing may be used by computers, hand-held devices, telephonecomputer systems, kiosks, and a wide variety of other devices to improvehuman-computer interactions.

BRIEF DESCRIPTION OF DRAWINGS

For a more complete understanding of the present disclosure, referenceis now made to the following description taken in conjunction with theaccompanying drawings.

FIG. 1 illustrates a system for recognizing a user that speaks anutterance according to embodiments of the present disclosure.

FIG. 2 is a conceptual diagram of how a spoken utterance may beprocessed according to embodiments of the present disclosure.

FIG. 3 is a conceptual diagram of a system architecture for parsingincoming utterances using multiple domains according to embodiments ofthe present disclosure.

FIG. 4 is a conceptual diagram of how text-to-speech processing isperformed according to embodiments of the present disclosure.

FIG. 5 illustrates data stored and associated with user profilesaccording to embodiments of the present disclosure.

FIG. 6 is a flow diagram illustrating processing performed to prepareaudio data for ASR and user recognition according to embodiments of thepresent disclosure.

FIG. 7 is a diagram of a vector encoder according to embodiments of thepresent disclosure.

FIG. 8 is a system flow diagram illustrating user recognition accordingto embodiments of the present disclosure.

FIGS. 9A through 9C are a signal flow diagram illustrating determiningoutput content based on user recognition according to embodiments of thepresent disclosure.

FIG. 10 is a block diagram conceptually illustrating example componentsof a device according to embodiments of the present disclosure.

FIG. 11 is a block diagram conceptually illustrating example componentsof a server according to embodiments of the present disclosure.

FIG. 12 illustrates an example of a computer network for use with thesystem.

DETAILED DESCRIPTION

Automatic speech recognition (ASR) is a field of computer science,artificial intelligence, and linguistics concerned with transformingaudio data associated with speech into text representative of thatspeech. Similarly, natural language understanding (NLU) is a field ofcomputer science, artificial intelligence, and linguistics concernedwith enabling computers to derive meaning from text input containingnatural language. ASR and NLU are often used together as part of aspeech processing system. Text-to-speech (TTS) is a field of concerningtransforming textual data into audio data that is synthesized toresemble human speech.

Speech processing systems have become robust platforms enabled toperform a variety of speech related tasks such as playing music,controlling household devices, communicating with other users, shopping,etc. Speech processing systems may process a spoken utterance to obtaincontent responsive thereto (for example output music, news content, orthe like). Speech processing systems may also process a spokenutterance, and therefrom perform TTS processing to createcomputer-generated speech responsive to the spoken utterance thusenabling the system to engage in a conversation with a user and providefeedback and prompts in spoken form. For example, a user may speak arequest to play music and the system may respond, in a spoken form“playing music” before actually outputting the music content.

Some speech processing systems have access to multiple content sourcesstoring or having access to content responsive to spoken utterances.Each content source may have a respective user recognition confidencethreshold that must be satisfied prior to the content source providingaccess to the requested content. Identification of a user refers todetermining an identity of a user. Verification refers to confirming apreviously determined identity of a user. Recognition of users refers toboth identification and verification of a user. Various levels of userrecognition (e.g., the system's confidence that the user corresponds toa user profile, like “John Smith”) may be determined using differentcombinations of user recognition techniques. For example, voice analysison input audio data may be used to recognize the user. Facialrecognition may also be used to recognize the user. Still othertechniques such as input of a password, spoken passphrase, retina scan,fingerprint scan, etc. may also be used to recognize the user. Certainsingle techniques, or combinations of techniques, may result in thesystem having a higher confidence that the user corresponds to aparticular identity. As can be appreciated, however, the more suchtechniques are required, the more the user may need to do to recognizehimself/herself. Such recognitions may add friction to the user'sinteraction with the system and may be undesirable in certaincircumstances.

Further, the user recognition confidence threshold to interact withdifferent content sources may be different. For example, a speechprocessing system may be capable of generating output content in theform of various system-generated prompts using TTS that address the userby name such as “playing your music John,” or the like. To generate suchname-specific responses the system may require some low level ofconfidence that the user is John. The speech processing system may alsobe able to connect to a variety of other services such as music playingservices and/or a banking service. The music playing services may sendmusic data to the system if the system is mostly confident that therequesting user is who the system thinks the user is (and the user hasan account with the music service). The banking service, on the otherhand, may be willing to send data regarding branch locations to anyonebut may only be willing to send balance information if the system isvery confident that the requesting user is who the system thinks theuser is (and the profile associated with the user indicates it isauthorized to access the appropriate bank account). These differentlevels of confidence may correspond to different system or third-partysettings depending on the content desired, third party rules, etc.

In particular, certain single utterances may include a command thatwould result in multiple pieces of output content. For example, anutterance invoking a banking application may result in a request to thebanking application for output content related to an account balance aswell as a request for system generated TTS output content acknowledgingthe request (e.g., “obtaining your checking account balance John”). If,however, the system has only a medium confidence of the user's identity(i.e., that the user is “John”), then the system may be able to generatethe customized TTS content (as that requires low user recognitionconfidence) but may not be able to obtain the account balanceinformation (as that may require high user recognition confidence). Thismay result in the system returning a TTS prompt soliciting further userrecognition needed to access the account information, but such a TTSprompt may end up being personalized due to the low confidence neededfor a personalized TTS prompt. Thus the user may be output a TTSresponse such as “John, while I am pretty sure it is you, I need to becertain before granting access to your account. Please provide [specificinformation] for verification purposes.” While such a message may beacceptable from the machine, embodiments described herein can be used toenable more sophisticated machine-based interactions with the user.

The present disclosure improves traditional speech processing systemsby, among other things, grouping the content sources (e.g., NLU domains)needed to respond to or otherwise process a single utterance,determining a level of user verification (if any) that will be needed byeach content source, and performing user recognition with respect to amost stringent user recognition confidence threshold of the contentsource(s). Thus, when a single spoken utterance results in content frommultiple content sources, where each content source has a respectiveuser recognition confidence threshold that must be satisfied, the systemmay refrain from outputting user-customized content in response to theutterance unless the highest user recognition confidence threshold ofthe grouped content sources can be satisfied. The system may performuser recognition and provide all the invoked content sources with anindication of user recognition only if the most stringent userrecognition confidence threshold of the content sources is satisfied.This may decrease a likelihood of undesirable operation of the speechprocessing system without increasing unintentional disclosure of data tounauthorized users. Alternatively, the system can provide a customizedresponse if the confidence value exceeds the personalized responsethreshold and explain to the user that additional level of confidence isneeded to meet the highest user verification confidence threshold of thegrouped content sources needed to fully process the inquiry.

FIG. 1 illustrates a system 100 configured to recognize a user accordingto the present disclosure. Although the figures and discussionillustrate certain operational steps of the system 100 in a particularorder, the steps described may be performed in a different order (aswell as certain steps removed or added) without departing from theintent of the disclosure. As shown in FIG. 1, the system 100 may includea speech-controlled device 110 local to a user 5, and a server(s) 120connected to the speech-controlled device 110 across a network(s) 199.The server(s) 120 (which may be one or more different physical devices)may be capable of performing speech processing (e.g., ASR, NLU, commandprocessing, etc.), TTS processing, and user recognition as describedherein. A single server 120 may perform all speech processing, TTSprocessing, and user recognition. Alternatively, multiple servers 120may combine to perform all speech processing, TTS processing, and userrecognition. Further, the server(s) 120 may execute certain commands,such as answering spoken utterances of the user 5. In addition, certainspeech detection or command execution functions may be performed by thespeech-controlled device 110.

As illustrated in FIG. 1, during a training phase, the speech-controlleddevice 110 may capture various speech (i.e., input audio 11) of the user5 via a microphone 103 of the speech-controlled device 110. For example,capturing of the training speech may occur as part of enrolling the userwith the speech-controlled device 110/system 100. The speech-controlleddevice 110 may then send training data corresponding to the trainingspeech to the server(s) 120. Alternatively, a microphone array (notillustrated), separate from the speech-controlled device 110, maycapture the training speech. In an example, the microphone array is indirect communication with the speech-controlled device 110 such thatwhen the microphone array captures the training speech, the microphonearray sends the training data to the speech-controlled device 110. Thespeech-controlled device 110 may then forward the received training datato the server(s) 120. In another example, the microphone array is inindirect communication with the speech-controlled device 110 via acompanion application of a mobile computing device, such as a smartphone, tablet, laptop, etc. In this example, when the microphone arraycaptures the training speech, the microphone array sends the trainingdata to the companion application, which forwards the training data tothe speech-controlled device 110. The speech-controlled device 110 maythen forward the training data to the server 120. In yet anotherexample, the microphone array is in indirect communication with theserver 120 via the companion application such that when the microphonearray captures the training speech, the microphone array sends thetraining data to the companion application, which forwards the trainingdata to the server 120.

The server(s) 120 receives (150) the training data and associates (152)the training data with the user 5 in a user profile associated with thespeech-controlled device 110 from which the training data originated. Itshould be appreciated that the server(s) 120 may receive (150) trainingdata from multiple speech-controlled devices 110 of the system, and maystore the training data with respective users and user profiles.

The server(s) 120 may simply store, in the user profile, waveforms oftraining data without determining features/vectors of the training data.In this example, features/vectors of the training data may be determinedeach time the server(s) 120 attempts to compare features/vectors of aspoken utterance to the training data. Alternatively, upon receiving(150) training data, the server(s) 120 may determine features/vectors ofthe training data and associate (152) the features/vectors with the userin the user profile. This allows the server(s) 120 to only determine thefeatures/vectors of the training data once, thereby negating duplicativeprocessing.

During runtime, as shown in FIG. 1, the microphone 103 of thespeech-controlled device 110 (or a separate microphone array dependingupon implementation) captures an utterance (i.e., input audio 11) spokenby the user 5. The server(s) 120 receives (154) input audio dataincluding the spoken utterance from the speech-controlled device 110 ora companion application (depending upon implementation as describedherein above). The server(s) 120 may determine (156) user recognitiondata indicating a confidence that a particular user(s) of thespeech-controlled device 110 spoke the utterance. The server(s) 120 mayalso determine (158) multiple content sources storing or having accessto content responsive to the spoken utterance. For example, a singlespoken utterance of “What is the weather?” may call for one contentsource to determine the weather data (e.g., sunny) and another contentsource to create a TTS result to personalize a response to the userpresenting the weather data (e.g., “Hi John, it is presently sunny”).The server(s) 120 may determine (160) a user recognition confidencethreshold associated with each of the determined content sources. Theuser recognition confidence thresholds may be established during atraining phase of the system 100. A user recognition confidencethreshold may be adjusted during runtime of the system 100. For example,if a user recognition confidence threshold is routinely not satisfiedduring runtime, the system 100 may decrease the threshold, and viceversa. A first content source (e.g., the TTS response generator) may beconfigured to provide content related to the greeting “Hi John” while asecond content source (e.g., a weather application server 125) may beconfigured to provide content related to “it is presently sunny.” Thefirst content source may require a first user recognition confidencethreshold based on the specificity of the greeting (i.e., the greetingbeing specific to the present user of the speech-controlled device 110,that is “John”) while the second content source may require a second,lower user recognition confidence threshold. The server(s) 120 mayreceive indicators of the respective user recognition confidencethresholds from each respective content source in the form of userrecognition confidence threshold data. Alternatively, the server(s) 120may determine a user recognition confidence threshold of each contentsource by accessing a storage including user recognition confidencethreshold data associated with respective content sources. The server(s)120 may send (162) the determined user recognition data to all of thedetermined content sources (i.e., the content sources having access tocontent responsive to the spoken utterance) only if the user recognitiondata satisfies a most stringent user recognition confidence threshold ofthe content sources used to satisfy a command of the utterance (e.g., arequest for weather data). For example, if a first content source has auser recognition confidence threshold of low, a second content sourcehas a user recognition confidence threshold of medium and a thirdcontent source has a user recognition confidence threshold of high, theserver(s) 120 may only send the determined user recognition data to thefirst content source, the second content source, and the third contentsource if the determined user recognition data includes a userrecognition confidence of “high” or higher. If the most stringent userrecognition confidence threshold is not met, the system may stillexecute the command of the utterance, however it may do so withoutcertain user-customized operations that may otherwise have beenperformed. Thus while certain content data may still be output to theuser, it may not be as personalized to the user as it might have been ifthe confidence was higher. (For example, weather data may be returned,but the system may not address the user by name when reading out theweather data.)

As described above, user recognition may be performed using trainingdata captured while enrolling the user with the system100/speech-controlled device 110. However, it should be appreciated thatuser recognition may be performed without using training data capturedduring an enrollment process. For example, reference data used toperform user recognition may be captured during runtime (i.e., when theuser interacts with the system 100 at runtime by, for example, speakingcommands).

Further details of the system 100 configured to verify a user thatspeaks an utterance are explained below, following a discussion of theoverall speech processing system of FIG. 2. The system 100 may operateusing various speech processing components as described in FIG. 2. FIG.2 is a conceptual diagram of how a spoken utterance is processed. Thevarious components illustrated may be located on a same or differentphysical devices. Communication between various components illustratedin FIG. 2 may occur directly or across a network 199. An audio capturecomponent, such as the microphone 103 of the speech-controlled device110 (or other device), captures input audio 11 corresponding to a spokenutterance. The device 110, using a wakeword detection module 220, thenprocesses audio data corresponding to the input audio 11 to determine ifa keyword (such as a wakeword) is detected in the audio data. Followingdetection of a wakeword, the speech-controlled device 110 sends audiodata 111, corresponding to the utterance, to a server 120 that includesan ASR module 250. The audio data 111 may be output from an acousticfront end (AFE) 256 located on the device 110 prior to transmission, orthe audio data 111 may be in a different form for processing by a remoteAFE 256, such as the AFE 256 located with the ASR module 250.

The wakeword detection module 220 works in conjunction with othercomponents of the device 110, for example the microphone 103 to detectkeywords in audio data corresponding to the input audio 11. For example,the device 110 may convert input audio 11 into audio data, and processthe audio data with the wakeword detection module 220 to determinewhether speech is detected, and if so, if the audio data comprisingspeech matches an audio signature and/or model corresponding to aparticular keyword.

The device 110 may use various techniques to determine whether audiodata includes speech. Some embodiments may apply voice activitydetection (VAD) techniques. Such techniques may determine whether speechis present in input audio based on various quantitative aspects of theinput audio, such as a spectral slope between one or more frames of theinput audio; energy levels of the input audio in one or more spectralbands; signal-to-noise ratios of the input audio in one or more spectralbands; or other quantitative aspects. In other embodiments, the device110 may implement a limited classifier configured to distinguish speechfrom background noise. The classifier may be implemented by techniquessuch as linear classifiers, support vector machines, and decision trees.In still other embodiments, Hidden Markov Model (HMM) or GaussianMixture Model (GMM) techniques may be applied to compare the input audioto one or more acoustic models in speech storage, which acoustic modelsmay include models corresponding to speech, noise (such as environmentalnoise or background noise), or silence. Still other techniques may beused to determine whether speech is present in the input audio.

Once speech is detected in the input audio, the device 110 may use thewakeword detection module 220 to perform wakeword detection to determinewhen a user intends to speak a command to the device 110. This processmay also be referred to as keyword detection, with the wakeword being aspecific example of a keyword. Specifically, keyword detection istypically performed without performing linguistic analysis, textualanalysis, or semantic analysis. Instead, incoming audio (or audio data)is analyzed to determine if specific characteristics of the audio matchpreconfigured acoustic waveforms, audio signatures, or other data todetermine if the incoming audio “matches” stored audio datacorresponding to a keyword.

Thus, the wakeword detection module 220 may compare audio data to storedmodels or data to detect a wakeword. One approach for wakeword detectionapplies general large vocabulary continuous speech recognition (LVCSR)systems to decode the audio signals, with wakeword searching conductedin the resulting lattices or confusion networks. LVCSR decoding mayrequire relatively high computational resources. Another approach forwakeword spotting builds HMMs for each wakeword and non-wakeword speechsignals respectively. The non-wakeword speech includes other spokenwords, background noise, etc. There can be one or more HMMs built tomodel the non-wakeword speech characteristics, which are named fillermodels. Viterbi decoding is used to search the best path in the decodinggraph, and the decoding output is further processed to make the decisionon keyword presence. This approach can be extended to includediscriminative information by incorporating a hybrid deep neural network(DNN)-HMM decoding framework. In another embodiment the wakewordspotting system may be built on DNN/recursive neural network (RNN)structures directly, without HMM involved. Such a system may estimatethe posteriors of wakewords with context information, either by stackingframes within a context window for DNN, or using RNN. Follow-onposterior threshold tuning or smoothing is applied for decision making.Other techniques for wakeword detection, such as those known in the art,may also be used.

Once the wakeword is detected, the local device 110 may “wake” and begintransmitting audio data 111 corresponding to input audio 11 to theserver(s) 120 for speech processing (e.g., for purposes of executing acommand in the speech). The audio data 111 may include datacorresponding to the wakeword, or the portion of the audio datacorresponding to the wakeword may be removed by the local device 110prior to sending the audio data 111 to the server 120.

Upon receipt by the server(s) 120, an ASR module 250 may convert theaudio data 111 into text data. The ASR module 250 transcribes the audiodata 111 into text data representing words of speech contained in theaudio data 111. The text data may then be used by other components forvarious purposes, such as executing system commands, inputting data,etc. A spoken utterance in the audio data 111 is input to a processorconfigured to perform ASR, which then interprets the spoken utterancebased on a similarity between the spoken utterance and pre-establishedlanguage models 254 stored in an ASR model knowledge base (i.e., ASRmodel storage 252). For example, the ASR module 250 may compare theaudio data 111 with models for sounds (e.g., subword units or phonemes)and sequences of sounds to identify words that match the sequence ofsounds spoken in the spoken utterance of the audio data 111.

The different ways a spoken utterance may be interpreted (i.e., thedifferent hypotheses) may each be assigned a respectiveprobability/confidence score representing a likelihood that a particularset of words matches those spoken in the spoken utterance. Theconfidence score may be based on a number of factors including, forexample, a similarity of the sound in the spoken utterance to models forlanguage sounds (e.g., an acoustic model 253 stored in the ASR modelstorage 252), and a likelihood that a particular word that matches thesound would be included in the sentence at the specific location (e.g.,using a language model 254 stored in the ASR model storage 252). Thus,each potential textual interpretation of the spoken utterance (i.e.,hypothesis) is associated with a confidence score. Based on theconsidered factors and the assigned confidence score, the ASR module 250outputs the most likely text recognized in the audio data 111. The ASRmodule 250 may also output multiple hypotheses in the form of a latticeor an N-best list with each hypothesis corresponding to a confidencescore or other score (e.g., such as probability scores, etc.).

The device or devices including the ASR module 250 may include an AFE256 and a speech recognition engine 258. The AFE 256 transforms theaudio data 111 into data for processing by the speech recognition engine258. Such transformation is discussed in further detail with regard toFIG. 6 below. The speech recognition engine 258 compares the speechrecognition data with acoustic models 253, language models 254, andother data models and information for recognizing the speech conveyed inthe audio data 111. The AFE 256 may reduce noise in the audio data 111and divide the digitized audio data 111 into frames representing timeintervals for which the AFE 256 determines a number of values (i.e.,features) representing qualities of the audio data 111, along with a setof those values (i.e., a feature vector or audio feature vector)representing features/qualities of the audio data 111 within each frame.In one configuration each audio frame includes 25 ms of audio and theframes start at 10 ms intervals resulting in a sliding window whereadjacent audio frames include 15 ms of overlapping audio. Many differentfeatures may be determined, as known in the art, and each featurerepresents some quality of the audio data 111 that may be useful for ASRprocessing. A number of approaches may be used by the AFE 256 to processthe audio data 111, such as mel-frequency cepstral coefficients (MFCCs),perceptual linear predictive (PLP) techniques, neural network featurevector techniques, linear discriminant analysis, semi-tied covariancematrices, or other approaches known to those skilled in the art.

The speech recognition engine 258 may process data output from the AFE256 with reference to information stored in the ASR model storage 252.Alternatively, post-AFE processed data (e.g., feature vectors) may bereceived by the device executing ASR processing from another sourcebesides the internal AFE 256. For example, the speech-controlled device110 may process audio data 111 into feature vectors (e.g., using anon-device AFE 256) and transmit the feature vector data to the server120 across the network 199 for ASR processing. Feature vector data mayarrive at the server 120 encoded, in which case it may be decoded priorto processing by the processor executing the speech recognition engine258.

The speech recognition engine 258 attempts to match received featurevectors to language phonemes and words as known in the stored acousticmodels 253 and language models 254. The speech recognition engine 258computes recognition scores for the feature vectors based on acousticinformation and language information. The acoustic information is usedto calculate an acoustic score representing a likelihood that theintended sound represented by a group of feature vectors matches alanguage phoneme. The language information is used to adjust theacoustic score by considering what sounds and/or words are used incontext with each other, thereby improving a likelihood that the ASRmodule 250 will output speech results that make sense grammatically.

The speech recognition engine 258 may use a number of techniques tomatch feature vectors to phonemes, for example using HMMs to determineprobabilities that feature vectors may match phonemes. Sounds receivedmay be represented as paths between states of the HMM and multiple pathsmay represent multiple possible text matches for the same sound.

Following ASR processing, the ASR results may be sent by the speechrecognition engine 258 to other processing components, which may belocal to the device performing ASR and/or distributed across thenetwork(s) 199. For example, ASR results in the form of a single textualrepresentation of the speech, an N-best list including multiplehypotheses and respective scores, lattice, etc. may be sent to a server,such as the server 120, for natural language understanding (NLU)processing, such as conversion of the text data into commands forexecution, either by the speech-controlled device 110, by the server120, or by another device (e.g., a server running a search engine,etc.). For example, the ASR component 250 may output text data 300 forfurther processing by an NLU 260, where the text data 300 may include asingle top scoring hypothesis or a N-best list including multiplehypotheses. Further, the ASR component 250 may output ASR confidencescore data 807 for further processing by a user verification module 802(discussed below) or other component. The ASR confidence score data 807may include a respective score for each hypothesis in an N-best list ormay include a single score for the top hypothesis output as the textdata 300. In other configurations the ASR confidence score data 807 mayinclude general confidence data, such as one or more values thatindicate how generally confident the ASR component 250 was in itsprocessing, without necessarily linking that confidence to a specifichypothesis. The ASR confidence score data 807 may be based on variousfactors such as audio quality, whether the hypotheses had similar scoresor whether one hypothesis largely outscored the others, or otherfactors.

The device performing NLU processing (e.g., the server 120) may includevarious components, including potentially dedicated processor(s),memory, storage, etc. The device performing NLU processing may include adedicated NLU module/component 260, which may include a named entityrecognition (NER) module 262, and intent classification (IC) module 264.The device performing NLU processing may additionally include NLUstorage 273, and a knowledge base (not illustrated). The knowledge baseis a database or other information storage that may include informationabout entities that may be used in resolving spoken utterances. The NLUmodule 260 may also utilize gazetteer information 284 stored in anentity library storage 282. The knowledge base and/or gazetteerinformation 284 may be used for entity resolution, for example matchingASR results with different entities (e.g., song titles, contact names,etc.). Gazetteers 284 may be linked to users (e.g., a particulargazetteer may be associated with a specific user's music collection),may be linked to certain domains (e.g., shopping), or may be organizedin a variety of other ways.

The NLU module 260 takes text data (e.g., output from the ASR module 250based on the input audio data 111) and attempts to make a semanticinterpretation of the text data. That is, the NLU module 260 determinesthe meaning behind the text data based on the individual words and thenimplements that meaning. The NLU module 260 interprets a text string toderive an intent or a desired action from the user as well as thepertinent pieces of information in the text data that allow a device(e.g., the speech-controlled device 110, the server 120, an applicationserver, etc.) to complete that action. For example, if a spokenutterance is processed using the ASR module 250, which outputs the textdata “call mom”, the NLU module 260 may determine the user intended toactivate a telephone in his/her device and to initiate a call with acontact matching the entity “mom.”

The NLU module 260 may process several textual inputs related to thesame utterance. For example, if the ASR module 250 outputs N textsegments (e.g., as part of an N-best list), the NLU module 260 mayprocess all N outputs to obtain NLU results.

The NLU module 260 may be configured to parse and tag to annotate textdata as part of NLU processing. For example, for the text data “callmom,” “call” may be tagged as a command (e.g., to execute a phone call)and “mom” may be tagged as a specific entity and target of the command.In addition, the telephone number for the entity corresponding to “mom”stored in a contact list may be included in the annotated NLU results.

To correctly perform NLU processing of speech input, the NLU module 260may be configured to determine a “domain” of the utterance so as todetermine and narrow down which services offered by an endpoint device(e.g., the server 120, the speech-controlled device 110, an applicationserver, etc.) may be relevant. For example, an endpoint device may offerservices relating to interactions with a telephone service, a contactlist service, a calendar/scheduling service, a music player service,etc. Words in text data may implicate more than one service, and someservices may be functionally linked (e.g., both a telephone service anda calendar service may utilize data from the contact list).

The NER module 262 receives an utterance in the form of ASR results andattempts to identify relevant grammars and lexical information that maybe used to construe meaning. To do so, the NER module 262 may begin byidentifying potential domains that may relate to the received utterance.The NLU storage 273 includes a database of domains 274 associated withspecific devices. For example, the speech-controlled device 110 may beassociated with domains for music, telephony, calendaring, contactlists, and device-specific communications. In addition, the entitylibrary 282 may include database entries about specific services on aspecific device, either indexed by Device ID, User ID, Household ID, orsome other indicator.

A domain may represent a discrete set of activities having a commontheme, such as “shopping”, “music”, “calendaring”, etc. As such, eachdomain may be associated with a particular language model and/or grammardatabase 276, a particular set of intents/actions 278, and/or aparticular personalized lexicon 286. Each gazetteer 284 may includedomain-indexed lexical information associated with a particular userand/or device. For example, the Gazetteer A 284 a includes domain-indexlexical information 286 aa to 286 an. A user's music-domain lexicalinformation might include album titles, artist names, and song names,for example, whereas a user's contact-list lexical information mightinclude the names of contacts. Since every user's music collection andcontact list is presumably different, this personalized informationimproves entity resolution.

An utterance may be processed applying the rules, models, andinformation applicable to each identified domain. For example, if anutterance potentially implicates both communications and music, theutterance will be NLU processed using the grammar models and lexicalinformation for communications, and will also be processed using thegrammar models and lexical information for music. The responses to thespoken utterance produced by each set of models is scored, with theoverall highest ranked result from all applied domains being ordinarilyselected to be the correct result. This is described further in detailbelow in reference to FIG. 3.

An IC module 264 parses the utterance to determine an intent(s) for eachidentified domain, where the intent(s) corresponds to the action to beperformed that is responsive to the spoken utterance. Each domain isassociated with a database 278 of words linked to intents. For example,a music intent database may link words and phrases such as “quiet,”“volume off,” and “mute” to a “mute” intent. The IC module 264identifies potential intents for each identified domain by comparingwords in the utterance to the words and phrases in the intents database278.

In order to generate a particular interpreted response, the NER module262 applies the grammar models and lexical information associated withthe respective domain. Each grammar model 276 includes the names ofentities (i.e., nouns) commonly found in speech about the particulardomain (i.e., generic terms), whereas the lexical information 286 fromthe gazetteer 284 is personalized to the user and/or the device. Forinstance, a grammar model 276 associated with a shopping domain mayinclude a database of words commonly used when people discuss shopping.

The intents identified by the IC module 264 are linked todomain-specific grammar frameworks (included in 276) with “slots” or“fields” to be filled. For example, if “play music” is an identifiedintent, a grammar framework(s) may correspond to sentence structuressuch as “Play {Artist Name},” “Play {Album Name},” “Play {Song name},”“Play {Song name} by {Artist Name},” etc. However, to make recognitionmore flexible, these frameworks would ordinarily not be structured assentences, but rather based on associating slots with grammatical tags.

For example, the NER module 262 may parse the spoken utterance toidentify words as subject, object, verb, preposition, etc., based ongrammar rules and models, prior to recognizing named entities. Theidentified verb may be used by the IC module 264 to identify intent,which is then used by the NER module 262 to identify frameworks. Aframework for an intent of “play” may specify a list of slots/fieldsapplicable to play the identified “object” and any object modifier(e.g., a prepositional phrase), such as {Artist Name}, {Album Name},{Song name}, etc. The NER module 262 then searches the correspondingfields in the domain-specific and personalized lexicon(s), attempting tomatch words and phrases in the utterance tagged as a grammatical objector object modifier with those identified in the database(s).

This process includes semantic tagging, which is the labeling of a wordor combination of words according to their type/semantic meaning.Parsing may be performed using heuristic grammar rules, or the NERmodule 262 may be constructed using techniques such as HMMs, maximumentropy models, log linear models, conditional random fields (CRF), andthe like.

For instance, an utterance of “play mother's little helper by therolling stones” might be parsed and tagged as {Verb}: “Play,” {Object}:“mother's little helper,” {Object Preposition}: “by,” and {ObjectModifier}: “the rolling stones.” At this point in the process, “Play” isidentified as a verb based on a word database associated with the musicdomain, which the IC module 264 will determine corresponds to the “playmusic” intent. No determination has been made as to the meaning of“mother's little helper” and “the rolling stones,” but based on grammarrules and models, it is determined that these phrases relate to thegrammatical object of the spoken utterance.

The frameworks linked to the intent are then used to determine whatdatabase fields should be searched to determine the meaning of thesephrases, such as searching a user's gazette for similarity with theframework slots. So a framework for “play music intent” might indicateto attempt to resolve the identified object based on {Artist Name},{Album Name}, and {Song name}, and another framework for the same intentmight indicate to attempt to resolve the object modifier based on{Artist Name}, and resolve the object based on {Album Name} and {SongName} linked to the identified {Artist Name}. If the search of thegazetteer does not resolve the slot/field using gazetteer information,the NER module 262 may search the database of generic words associatedwith the domain (i.e., in the NLU storage 273). For instance, if theutterance was “play songs by the rolling stones,” after failing todetermine an album name or song name called “songs” by “the rollingstones,” the NER module 262 may search the domain vocabulary for theword “songs.” In the alternative, generic words may be checked beforethe gazetteer information, or both may be tried, potentially producingtwo different results.

The comparison process used by the NER module 262 may classify (i.e.,score) how closely a database entry compares to a tagged utterance wordor phrase, how closely the grammatical structure of the utterancecorresponds to the applied grammatical framework, and based on whetherthe database indicates a relationship between an entry and informationidentified to fill other slots of the framework.

The NER module 262 may also use contextual operational rules to fillslots. For example, if a user had previously requested to pause aparticular song and thereafter requested that the speech-controlleddevice 110 “please un-pause my music,” the NER module 262 may apply aninference-based rule to fill a slot associated with the name of the songthat the user currently wishes to play—namely the song that was playingat the time the user requested to pause the music.

The results of NLU processing may be tagged to attribute meaning to theutterance. So, for example, “play mother's little helper by the rollingstones” might produce a result of: {domain} Music, {intent} Play Music,{artist name} “rolling stones,” {media type} SONG, and {song title}“mother's little helper.” As another example, “play songs by the rollingstones” might produce: {domain} Music, {intent} Play Music, {artistname} “rolling stones,” and {media type} SONG.

The output from the ASR component 250 may be sent to a user recognitionmodule 802. The user recognition module 802 performs user recognitionusing the audio data 111, and optionally the ASR component output. Theuser recognition module 802 may include a scoring component thatdetermines respective scores indicating whether the input utterance inthe audio data 111 was spoken by particular users. The user recognitionmodule 802 may also include a confidence component that determines anoverall confidence as the accuracy of user recognition operations.

The output from the NLU processing, which may include tagged text data,commands, etc., and output from the user recognition module 802 (e.g.,user recognition confidence data) may then be sent to a commandprocessor 290, which may be located on a same or separate server 120 aspart of the system 100. The system 100 may include more than one commandprocessor 290, and the command processor 290 may be determined based onthe NLU output. For example, if the NLU output includes a command toplay music, the command processor 290 selected may correspond to a musicplaying application, such as one located on the speech-controlled device110 or in a music playing appliance, configured to execute a musicplaying command. Many such command processors 290 may be available tothe system depending on the various applications that may be invoked. Ifthe NLU output includes a search utterance (e.g., requesting the returnof search results), the command processor 290 selected may include asearch engine processor, such as one located on a search server,configured to execute a search command and determine search results,which may include output text data to be processed by a TTS engine andoutput from a device as synthesized speech.

The NLU operations of the system 100 may take the form of a multi-domainarchitecture, such as that illustrated in FIG. 3. In the illustratedarchitecture, each domain (which may include a set of intents and entityslots that define a larger concept such as music, books etc. as well ascomponents such as trained models, etc. used to perform various NLUoperations such as NER, IC, or the like) may be constructed separatelyand made available to the NLU component 260 during runtime operationswhere NLU operations are performed on text (such as text output from theASR component 250). Each domain may have specially configured componentsto perform various steps of the NLU operations.

For example, the system 100 may include a multi-domain architectureconsisting of multiple domains for intents/commands executable by thesystem 100 (or by other devices connected to the system 100), such asmusic, video, books, and information. The system 100 may include aplurality of domain recognizers 335, where each domain may include itsown recognizer 263. Each recognizer may include various NLU componentssuch as an NER component 262, IC module 264 and other components such asan entity resolver, or other components.

For example, a music domain recognizer 263-A (Domain A) may have an NERcomponent 262-A that identifies what slots (i.e., portions of input textdata 300) may correspond to particular words relevant to that domain.The words may correspond to entities such as (for the music domain) aperformer, album name, song name, etc. An NER component 262 may use amachine learning model, such as a domain specific conditional randomfield (CRF) to both identify the portions corresponding to an entity aswell as identify what type of entity corresponds to the text dataportion. For example, for the text “play songs by the stones,” an NER262-A trained for a music domain may recognize the portion of text [thestones] corresponds to an entity and an artist name. The music domainrecognizer 263-A may also have its own intent classification (IC)component 264-A that determines the intent of the text assuming that thetext is within the proscribed domain. An IC component 264 may use amodel, such as a domain specific maximum entropy classifier to identifythe intent of the text, where the intent is the action the user desiresthe system 100 to perform.

As illustrated in FIG. 3, multiple domains may operate substantially inparallel, with different domain specific components. That is, domain Bfor video may have its own recognizer 263-B including NER component262-B, and IC module 264-B. Domain C for books may also have similarcomponents in its recognizer 263-C, and so on for the different domainsavailable to the system. When input text data 300 (e.g., ASR output textdata) is received, the same text data that is input into the NLUpipeline for domain A 263-A may also be input into the NLU pipeline fordomain B 263-B, where the components for domain B 263-B will operate onthe text data as if the text data related to domain B, and so on for thedifferent NLU pipelines for the different domains. Each domain specificNLU pipeline will create its own domain specific NLU results, forexample NLU results A (for domain A), NLU results B (for domain B), NLUresults C (for domain C), and so on. The different NLU results may thenbe ranked and further processed using other downstream components asexplained below.

As shown in FIG. 3, an NER component 262 and IC module 264 may beconsidered part of a recognizer (such as recognizer 263-A, 263-B, 263-C,etc.). The recognizers may operate using machine learning trained modelssuch as a CRF, maximum entropy classifier, neural network (such as adeep neural network (DNN) or recurrent neural network (RNN)), or otherclassifier. The recognizers 335 may also use rules that operate on inputquery text data in a particular form to identify named entities and/orintents. The recognizers 335 may also operate using a data structuresuch as a finite state transducer (FST) to process the query text datato perform NER and/or IC. Other techniques or models may also be used toperform NER and/or IC. The techniques may also be used together. Forexample, a set of rules, an FST, and a trained machine learning modelmay all operate on input text data substantially in parallel todetermine the named entities/intents of an input utterance. If onetechnique performs its task with high enough confidence, the system 100may use the output of that technique over the others. The system 100 mayalso prioritize the results of different techniques in certaincircumstances (for example rules results may be higher priority than FSTresults, which may be higher priority than model results, or some otherconfiguration). Each recognizer (such as 263-A, 263-B, 263-C, etc.) mayhave its own rules, FSTs, and/or models operating such that eachrecognizer operates substantially in parallel to the other recognizersto come up with its own interpretation of the input text data.

The output of each recognizer 335 is a N-best list 340 of intents andidentified slots representing the particular recognizer's top choices asto the meaning of the input text data, along with scores for each itemin the N-best list 340. For example, for input text data 300 of “playpoker face by lady gaga,” the music domain recognizer 263-A may outputan N-best list 340 in the form of:

[0.95] PlayMusicIntent ArtistName: Lady Gaga SongName: Poker Face

[0.02] PlayMusicIntent ArtistName: Lady Gaga

[0.01] PlayMusicIntent ArtistName: Lady Gaga AlbumName: Poker Face

[0.01] PlayMusicIntent SongName: Pokerface

where the NER component 262-A of the recognizer 263-A has determinedthat for different items in the N-best list 340, the words “poker face”correspond to a slot and the words “lady gaga” correspond to a slot.(Though different items in the N-best list 340 interpret those slotsdifferently, for example labeling “poker face” as a song name in onechoice but labeling it as an album name in another.) The IC module 264-Aof the recognizer 263-A has also determined that the intent of the inputtext data 300 is a PlayMusicIntent (and selected that as the intent foreach item on the music N-best list 340). The recognizer 263-A alsodetermined a score for each item on the list representing therecognizer's confidence that the particular item is correct. As can beseen in the example, the top item has the highest score. Each recognizerof the recognizers 335 may operate on the input text data 300substantially in parallel, resulting in a number of different N-bestlists 340, one for each domain (e.g., one N-best 340 list for music, oneN-best list 340 for video, etc.). The size of any particular N-best list340 output from a particular recognizer is configurable and may bedifferent across domains.

While the recognizers 335 perform NER, that is they may identify wordsof the input text data 300 that are important for downstream processing(sometimes called light slot filling), and may even label those wordsaccording to type (such as artist name, album name, city, or the like),the recognizers 335 may not perform entity resolution (i.e., determiningthe actual entity corresponding to the words of the input text data300). Entity resolution is typically a higher latency process andinvolves communications with a knowledge base 272 or other component toprecisely identify the specific entities. As this process is resourceintensive, it may be preferable to not perform this task for each itemof every N-best list 340 across the multiple domains as some items havelow scores and are unlikely to be used and any resources spentperforming entity resolution would be wasted on low scoring items. Thus,a filtering of potential results may first be performed before engagingin more resource intensive processing. To that end, the cumulativeN-best lists 340 may be passed to a cross domain processing component355 which may then further rank the individual items on the N-best lists340 as well as perform other operations.

The cross domain processing component 355 may include a cross-domainranker 350. The cross-domain ranker 350 takes the group of N-best lists340 and selects from among the lists 340 the top choices to create a newN-best list 360 that may include items from different domains, but onlyincludes the highest scoring ones of those domains. The purpose of thecross-domain ranker 350 is to create a new list of top scoring potentialresults, so that downstream (more resource intensive) processes may onlyoperate on the top choices.

As an example of a multi-domain N-best list 360 created by thecross-domain ranker 350, take the example input text data 300 of “playthe hunger games.” The text data 300 may be processed by each of therecognizers 335, and each will output an N-best list 340, resulting in agroup of N-best lists 340 input into the cross domain processingcomponent 355. The cross-domain ranker 350 may then rank the individualitems among the N-best lists 340 to create a new N-best list 360. Forexample, the cross-domain ranker 350 may output an N-best list 360 inthe form of:

[0.78] Video PlayVideoIntent VideoName: The Hunger Games

[0.13] Books ReadBookIntent BookName: The Hunger Games

[0.07] Music PlayMusicIntent AlbumName: Hunger Games

where the top items from different N-best lists 340 from multipledomains are grouped into a single N-best list 360. As shown, the topscoring item is from the video domain 263-B, which includes the intent“playvideointent” and a slot labeled as video name corresponding to thetext “the hunger games.” The next item is from the books domain 263-C,and includes the intent “readbookintent” and a slot labeled as book namecorresponding to the text “the hunger games.” Each item in the N-bestlist 360 may also include a score. The size of the cross domain N-bestlist 360 is configurable.

While the cross-domain ranker 350 takes as input the N-best lists 340,it may also consider other information, such as other data 391.

The cross domain processing component 355 may also include a light slotfiller component 352. The light slot filler 352 can take informationfrom slots and alter it to make the data more easily processed bydownstream components The operations of the light slot filler 352 aretypically low latency operations that do not involve heavy operationssuch as reference to a knowledge base. The purpose of the light slotfiller 352 is to replace words with other words or values that may bemore easily understood by downstream components. For example, if inputtext data 300 included the word “tomorrow”, the light slot filler 352may replace the word “tomorrow” with an actual date for purposes ofdownstream processing. Similarly, a word “CD” may be replaced by a word“album.” The replaced words are then included in the cross domain N-bestlist 360.

The N-best list 360 is then output to a heavy slot filler and entityresolution component 370. The heavy slot filler and entity resolver 370can apply rules or other instructions to standardize labels or tokensfrom previous stages into an intent/slot representation. The precisetransformation may depend on the domain (for example, for a traveldomain a text mention of “Boston airport” may be transformed to thestandard BOS three-letter code referring to the airport). The resolver370 can refer to an authority source (such as a knowledge base 272) thatis used to specifically identify the precise entity referred to in theentity mention identified in the incoming text. Specific intent/slotcombinations may also be tied to a particular source, which may then beused to resolve the text (such as by providing information or a commandto be executed in response to a user query). For example, in the example“play songs by the stones,” the resolver 370 may reference to a personalmusic catalog, Amazon Music account, user profile (discussed in detailbelow), or the like. The output from the entity resolution component 370may include an altered N-best list that is based on the cross-domainN-best list 360 but also includes more detailed information about thespecific entities mentioned in the text data 300 (such as specificentity IDs) and/or more detailed slot data that can eventually be usedby a command processor 290 which may be incorporated into the samesystem components or pipeline or may be on a separate device incommunication with the system 100. While illustrated as a cross-domainresolver, multiple resolution components 370 may exist where aparticular resolution component 370 may be specific for one or moredomains.

As can be appreciated, the entity resolver 370 may not necessarily besuccessful in resolving every entity and filling every slot. This mayresult in incomplete results in the combined N-best list 360. Are-scorer and final ranker component 390 may consider such errors whendetermining how to rank the ultimate results for potential execution.For example, if an item of the N-best list 360 comes from a book domainand includes a read book intent, but the entity resolver 370 cannot finda book with a title matching the input text data 300, that particularresult may be re-scored by the final ranker 390 to be given a lowerscore. Each item considered by the final ranker 390 may also be assigneda particular confidence, where the confidence may be determined by arecognizer 335, cross domain processor 355, or by the final ranker 390itself. Those confidence scores may be used to determine how to rank theindividual NLU results represented in the N-best lists 340. Theconfidence scores may be affected by unfilled slots. For example, if onedomain is capable of filling a slot (i.e., resolving the word in theslot to an entity or other recognizable form) for an input query, theresults from that domain may have a higher confidence than those from adifferent domain that is not capable of filling a slot.

The final ranker 390 may be configured to apply re-scoring, biasing, orother techniques to obtain the most preferred ultimate result. To do so,the final ranker 390 may consider not only the NLU results of the N-bestlists 340, but may also consider other data 391. This other data 391 mayinclude a variety of information. For example, the other data 391 mayinclude application rating or popularity. For example, if oneapplication has a particularly high rating, the system 100 may increasethe score of results associated with that particular application. Theother data 391 may also include information about applications that havebeen specifically enabled by the user (as indicated in a user profile asdiscussed in detail below). NLU results from enabled applications may bescored higher than results from non-enabled applications. User historymay also be considered, such as if the user regularly uses a particularsupplemental application or does so at particular times of day. Date,time, location, weather, type of device 110, customer ID, context, andother information may also be considered. For example, the system 100may consider when any particular applications are currently active (suchas music being played, a game being played, etc.) between the system 100and device 110. The highest scoring result (or results in the case ofmultiple commands being in an utterance) may be passed to a downstreamcommand processor 290 for execution.

Following final ranking, the NLU module 260 may output NLU output data385. The NLU output data 385 may include an indicator of the command tobe executed along with data associated with the command, for example anindication that the command is “play music” and the music to be playedis “Adele.” The NLU output data 385 may be in the form of previous NLUdata such as item(s) in N-best list 340, item(s) in cross-domain N-bestlist 360, or the like. The NLU output data 385 may also be in a formatexecutable by the command processor 290. Multiple instances of NLUoutput data (e.g., 385 a-385 n) may be output.

A TTS module 414 may receive tagged text data from the command processor290, so the TTS module 414 may synthesize speech corresponding to thetext data. Speech may be synthesized by the TTS module 414 as describedbelow with respect to FIG. 4.

The TTS module 414 includes a TTS front end (TTSFE) 416, a speechsynthesis engine 418, and a TTS storage 420. The TTSFE 416 transformsinput text data (e.g., from the command processor 290) into a symboliclinguistic representation for processing by the speech synthesis engine418. The TTSFE 416 may also process tags or other data input to the TTSmodule 414 that indicate how specific words should be pronounced. Thespeech synthesis engine 418 compares the annotated phonetic units andinformation stored in the TTS storage 420 for converting the input textdata into speech (i.e., audio data). The TTSFE 416 and the speechsynthesis engine 418 may include their own controller(s)/processor(s)and memory, or they may use the controller/processor and memory of theserver 120, the speech-controlled device 110, or another device, forexample. Similarly, the instructions for operating the TTSFE 416 and thespeech synthesis engine 418 may be located within the TTS module 414,within the memory and/or storage of the server 120, thespeech-controlled device 110, or within an external device.

Text data input into the TTS module 414 may be sent to the TTSFE 416 forprocessing. The TTSFE 416 may include modules for performing textnormalization, linguistic analysis, and linguistic prosody generation.During text normalization, the TTSFE 416 processes the input text dataand generates standard/normalized text data, converting such things asnumbers, abbreviations (e.g., Apt., St., etc.), and symbols ($, %, etc.)into the equivalent of written out words.

During linguistic analysis, the TTSFE 416 analyzes language in thenormalized text data to generate a sequence of phonetic unitscorresponding to the text data. This process may be referred to asphonetic transcription. Phonetic units include symbolic representationsof sound units to be eventually combined and output by the system 100 asspeech. Various sound units may be used for dividing text data forpurposes of speech synthesis. The TTS module 414 may process speechbased on phonemes (i.e., individual sounds), half-phonemes, di-phones(i.e., the last half of one phoneme coupled with the first half of anadjacent phoneme), bi-phones (i.e., two consecutive phonemes),syllables, words, phrases, sentences, or other units. Each word may bemapped to one or more phonetic units. Such mapping may be performedusing a language dictionary stored by the system 100, for example in theTTS storage 420. The linguistic analysis performed by the TTSFE 416 mayalso identify different grammatical components such as prefixes,suffixes, phrases, punctuation, syntactic boundaries, or the like. Suchgrammatical components may be used by the TTS module 414 to craft anatural sounding audio waveform output. The language dictionary may alsoinclude letter-to-sound rules and other tools that may be used topronounce previously unidentified words or letter combinations that maybe encountered by the TTS module 414. Generally, the more informationincluded in the language dictionary, the higher quality the speechoutput.

Based on the linguistic analysis, the TTSFE 416 may then performlinguistic prosody generation where the phonetic units are annotatedwith desired prosodic characteristics, also called acoustic features,which indicate how the desired phonetic units are to be pronounced inthe eventual output speech. During this stage, the TTSFE 416 mayconsider and incorporate any prosodic annotations that accompanied thetext data input to the TTS module 414. Such acoustic features mayinclude pitch, energy, duration, and the like. Application of acousticfeatures may be based on prosodic models available to the TTS module414. Such prosodic models indicate how specific phonetic units are to bepronounced in certain circumstances. A prosodic model may consider, forexample, a phoneme's position in a syllable, a syllable's position in aword, a word's position in a sentence or phrase, neighboring phoneticunits, etc. As with the language dictionary, a prosodic model with moreinformation may result in higher quality speech output than prosodicmodels with less information.

The output of the TTSFE 416, referred to as a symbolic linguisticrepresentation, may include a sequence of phonetic units annotated withprosodic characteristics. This symbolic linguistic representation may besent to the speech synthesis engine 418, also known as a synthesizer,for conversion into an audio waveform of speech for output to an audiooutput device and eventually to a user. The speech synthesis engine 418may be configured to convert the input text data into high-qualitynatural-sounding speech in an efficient manner. Such high-quality speechmay be configured to sound as much like a human speaker as possible, ormay be configured to be understandable to a listener without attempts tomimic a precise human voice.

The speech synthesis engine 418 may perform speech synthesis using oneor more different methods. In one method of synthesis called unitselection, a unit selection engine 430 matches the symbolic linguisticrepresentation created by the TTSFE 416 against a database of recordedspeech, such as a database of a voice corpus (e.g., the TTS voice unitstorage 472). The unit selection engine 430 matches the symboliclinguistic representation against spoken audio units in the database.Matching units are selected and concatenated together to form a speechoutput. Each unit includes an audio waveform corresponding with aphonetic unit, such as a short .wav file of the specific sound, alongwith a description of the various acoustic features associated with the.wav file (e.g., its pitch, energy, etc.), as well as other information,such as where the phonetic unit appears in a word, sentence, or phrase,neighboring phonetic units, etc. Using all the information in the unitdatabase, the unit selection engine 430 may match units to the inputtext data to create a natural sounding waveform. The unit database mayinclude multiple examples of phonetic units to provide the system 100with many different options for concatenating units into speech. Onebenefit of unit selection is that, depending on the size of thedatabase, a natural sounding speech output may be generated. Asdescribed above, the larger the unit database of the voice corpus, themore likely the system 100 will be able to construct natural soundingspeech.

In another method of synthesis, called parametric synthesis, parameterssuch as frequency, volume, and noise are varied by a parametricsynthesis engine 432, a digital signal processor, or other audiogeneration device to create an artificial speech waveform output.Parametric synthesis uses a computerized voice generator, sometimescalled a vocoder. Parametric synthesis may use an acoustic model andvarious statistical techniques to match a symbolic linguisticrepresentation with desired output speech parameters. Parametricsynthesis may include the ability to be accurate at high processingspeeds, as well as the ability to process speech without large databasesassociated with unit selection, but also typically produces an outputspeech quality that may not match that of unit selection. Unit selectionand parametric techniques may be performed individually or combinedtogether and/or combined with other synthesis techniques to producespeech audio data output.

Parametric speech synthesis may be performed as follows. The TTS module414 may include an acoustic model, or other models, which may convert asymbolic linguistic representation into a synthetic acoustic waveform ofthe input text data based on audio signal manipulation. The acousticmodel includes rules which may be used by the parametric synthesisengine 432 to assign specific audio waveform parameters to inputphonetic units and/or prosodic annotations. The rules may be used tocalculate a score representing a likelihood that a particular audiooutput parameter(s) (e.g., frequency, volume, etc.) corresponds to theportion of the input symbolic linguistic representation received fromthe TTSFE 416.

The parametric synthesis engine 432 may use a number of techniques tomatch speech to be synthesized with input phonetic units and/or prosodicannotations. One common technique is using HMMs. HMMs may be used todetermine probabilities that audio output should match textual input.HMMs may be used to translate from parameters from the linguistic andacoustic space to parameters to be used by a vocoder (i.e., the digitalvoice encoder) to artificially synthesize the desired speech. UsingHMMs, a number of states are presented, in which the states togetherrepresent one or more potential acoustic parameters to be output to thevocoder and each state is associated with a model, such as a Gaussianmixture model. Transitions between states may also have an associatedprobability, representing a likelihood that a current state may bereached from a previous state. Sounds to be output may be represented aspaths between states of the HMM and multiple paths may representmultiple possible audio matches for the same input text data. Eachportion of text data may be represented by multiple potential statescorresponding to different known pronunciations of phonemes and theirparts (e.g., phoneme identity, stress, accent, position, etc.). Aninitial determination of a probability of a potential phoneme may beassociated with one state. As new text data is processed by the speechsynthesis engine 418, the state may change or stay the same, based onprocessing of the new text data. For example, the pronunciation of apreviously processed word might change based on later processed words. AViterbi algorithm may be used to find the most likely sequence of statesbased on the processed text data. The HMMs may generate speech inparametrized form including parameters such as fundamental frequency(f0), noise envelope, spectral envelope, etc. that are translated by avocoder into audio segments. The output parameters may be configured forparticular vocoders such as a STRAIGHT vocoder, TANDEM-STRAIGHT vocoder,harmonic plus noise (HNM) based vocoders, code-excited linear prediction(CELP) vocoders, GlottHMM vocoders, harmonic/stochastic model (HSM)vocoders, or others.

For example, to create the customized speech output of the system 100,the system 100 may be configured with multiple voice inventories 478(stored in the TTS voice unit storage 472), where each unit database isconfigured with a different “voice.” Such voice inventories may also belinked to user accounts, discussed below. For example, one voice corpusmay be stored to be used to synthesize whispered speech (or speechapproximating whispered speech), another may be stored to be used tosynthesize excited speech (or speech approximating excited speech), andso on. To create the different voice corpuses a multitude of TTStraining utterances may be spoken by an individual and recorded by thesystem 100. The TTS training utterances used to train a TTS voice corpusmay be different from the training utterances used to train an ASRsystem. The audio associated with the TTS training utterances may thenbe split into small audio segments and stored as part of a voice corpus.The individual speaking the TTS training utterances may speak indifferent voice qualities to create the customized voice corpuses, forexample the individual may whisper the training utterances, say them inan excited voice, and so on. Thus, the audio data of each customizedvoice corpus may match a desired speech quality. The customized voiceinventory 478 may then be used during runtime to perform unit selectionto synthesize speech.

As an alternative to customized voice corpuses or customized parametric“voices,” one or more filters may be used to alter traditional TTSoutput to match a desired speech quality (e.g., whisper, shout, etc.).For example, the TTS module 414 may synthesize speech as normal, but thesystem 100, either as part of the TTS module 414 or otherwise, may applya filter to make the synthesized speech take on the desired speechquality. In this manner a traditional TTS output may be altered to takeon the desired speech quality.

During runtime the TTS module 414 may receive text data for speechsynthesis along with an indicator for a desired speech quality of theoutput speech. The TTS module 414 may then select a voice matching thespeech quality, either for unit selection or parametric synthesis, andsynthesize speech using the received text data and speech qualityindicator.

FIG. 5 illustrates a user profile storage 502 that includes dataregarding users of a device. The user profile storage 502 may be locatedproximate to the server(s) 120, or may otherwise be in communicationwith various components of the system 100, for example over the network199. The user profile storage 502 may include a variety of informationrelated to individual users, accounts, etc. that interact with thesystem 100. In an example, the user profile storage 502 is a cloud-basedstorage. For illustration, as shown in FIG. 5, the user profile storage502 may include data regarding multiple users of a singlespeech-controlled device 110 (or other device). Each user indicated in auser profile associated with a speech-controlled device 110 may beassociated with training data corresponding to training spokenutterances of the respective user, such as training data 805 discussedbelow. In addition or alternatively, each user indicated in the userprofile associated with a speech-controlled device 110 may be associatedwith feature/vector data corresponding to training data of therespective user. Further, each user may have a user ID that identifiesthe specific user for further system processing.

FIG. 6 illustrates processing performed to prepare audio data for ASRand user recognition. The speech-controlled device 110 sends audio data111 through a network(s) 199 to the server(s) 120 for processing. Theserver(s) 120 may include an acoustic front end (AFE) 256 (or othercomponent(s)) that performs various functions on the incoming audio data111 to prepare the incoming audio data 111 for further downstreamprocessing, such as ASR and/or user recognition. For example, the AFE256 may perform (602) windowing functions on the audio data 111 tocreate framed audio data 603 (e.g., waveforms). The size of each framemay depend upon implementation. In an example, each frame may includetwenty-five (25) milliseconds (m/s) of audio data, with an overlap ofthe next frame of 10 ms of data, thus resulting in sliding windowprocessing of audio data. Performing a windowing function may includemultiplying a time record by a finite-length window with an amplitudethat varies smoothly and gradually toward zero at its edges. Byperforming such, the endpoints of the waveforms of respective frames ofaudio data meet, resulting in a continuous waveform without sharptransitions. The AFE 256 may then perform (604) a fast Fourier transform(FFT) that converts the waveforms in each frame of the framed audio data603 from its original domain (e.g., time) to a representation in afrequency domain (thereby creating frequency domain framed audio data605). Audio processing techniques other than or in addition to FFT maybe used to transform audio data (e.g., waveforms) into data that can beprocessed as needed.

The server(s) 120 (through the AFE 256 or using another component) thendetects (610) whether voice activity (i.e., speech) is present in thepost-FFT waveforms (i.e., frequency domain framed audio data 605). Indoing so the server(s) 120 may perform VAD operations discussed above.The VAD detector 610 (or other components) may also be configured in adifferent order, for example the VAD detector 610 may operate on inputaudio data 111 rather than on frequency domain framed audio data 605,may operate on ASR features, etc. Various different configurations ofcomponents are possible. If there is no speech in the audio data, theserver(s) 120 discards the frequency domain framed audio data 605 (i.e.,removes the audio data from the processing stream). If, instead, theserver(s) 120 detects speech in the frequency domain framed audio data605, the server(s) 120, performs user recognition feature extraction(608) on the frequency domain framed audio data 605. User recognitionfeature extraction (608) may include performing frame level featureextraction and/or utterance level feature extraction. The frame levelfeature extraction may determine which frame of a universal backgroundmodel (UBM) the frame corresponds to. The UBM may be a Gaussian mixturemodel, a deep neural network, etc. The utterance level featureextraction may analyze aligned speech frames to derive feature vectorsof fixed length (i.e., the user recognition feature/vector data 609).The feature extraction may continue until voice activity is no longerdetected in the input audio data, at which point the server(s) 120 maydetermine that an endpoint of the speech has been reached processingwith respect thereto. ASR feature extraction (606) may be performed onall the audio data 111 received from the speech-controlled device 110.Alternatively (not illustrated), ASR feature extraction (606) may onlybe performed on audio data including speech (as indicated by the VAD610). ASR feature extraction (606) and user recognition featureextraction (608) involve determining values (i.e., features)representing qualities of the frequency domain framed audio data 605,along with quantitating those features into values (i.e., featurevectors or audio feature vectors). ASR feature extraction (606) maydetermine ASR feature/vector data 607 useful for ASR processing, anduser recognition feature extraction (608) may determine user recognitionfeature/vector data 609 useful for user recognition. The ASRfeature/vector data 607 and the user recognition feature/vector data 609may be the same features/vectors, different features/vectors, or mayinclude some overlapping features/vectors. A number of approaches may beused to extract features/vectors from the frequency domain framed audiodata 605, such as MFCCs, PLP techniques, neural network feature vectortechniques, linear discriminant analysis, semi-tied covariance matrices,or other approaches known to those skilled in the art.

Typically, the ASR feature/vector data 607 may include a different audiofeature vector for each audio frame. Thus, for one 25 ms long audioframe, the ASR feature extraction component 606 may output a single ASRfeature vector. The ASR feature vectors 607 output by the ASR featureextraction component 606 may be output to an ASR module 250 to performspeech recognition.

Depending on system configuration, the user recognition featureextraction component 608 may output multiple user recognition featurevectors, for example one such vector for each audio frame.Alternatively, the user recognition feature extraction component 608 maycontinue to input the frequency domain framed audio data 605 while theutterance is ongoing (e.g., before the endpoint of the speech is reachedin the audio data 605). While the audio data 605 for the utterance isinput, the user recognition feature extraction component 608 mayaccumulate or otherwise combine the audio data 605 as it comes in. Thatis, for a certain frame's worth of audio data 605 that comes in, theuser recognition feature extraction component 608 may combine thatframe's worth of data to the previous data received for the particularutterance. The particular method of accumulation may vary, includingusing an arithmetic component, a recurrent neural network (RNN), trainedmodel, or other component capable of combining audio data. Further, theform of combination performed by the user recognition feature extractioncomponent 608 may depend on what audio qualities are determined to beimportant for ultimate user recognition. Thus, the user recognitionfeature extraction component 608 may be trained to isolate and processdata that is most useful for user recognition. The output of the userrecognition feature extraction component 608 may thus include userrecognition feature/vector data 609 that includes values for featuresuseful for user recognition. The resulting user recognitionfeature/vector data 609 may then be used for user recognition.

The user recognition feature/vector data 609 may include multiplevectors each corresponding to different portions of the input utterance.Alternatively, the user recognition feature/vector data 609 may be asingle vector representing audio qualities of the input utterance.Referring to FIG. 7, the single vector may be created using an encoder750 which can create a fixed-size vector to represent certaincharacteristics of the audio data entities as described below. Inmathematical notation, given a sequence of feature data values x₁, . . .x_(n), . . . x_(N), with x_(n) being a D-dimensional vector, an encoderE(x₁, . . . x_(N))=y projects the feature sequence to y, with y being aF-dimensional vector. F is a fixed length of the vector and isconfigurable depending on use of the encoded vector and other systemconfigurations. As shown in FIG. 7, feature values 702 through 706(which may include feature vectors of audio data 111, frequency domainframed audio data 605, or the like) may be input into an encoder 750which will output an encoded feature vector 710 that represents theinput feature values. The VAD 610 may be an input into the encoder 750such that the encoder 750 may only operate when feature values inputtherein correspond to speech. The individual feature values (e.g.,702-706) may correspond to specific audio frames. Regardless of how manyfeature values are input, any particular encoder 750 will be configuredto output vectors of the same size, thus ensuring a continuity of outputencoded vector size from any particular encoder 750 (though differentencoders may output vectors of different fixed sizes) and enablingcomparing different feature vectors y. The value y may be called anembedding of the sequence x₁, . . . x_(N). The length of x_(n) and y arefixed and known a-priori, but the length of N of feature sequence x₁, .. . x_(N) is not necessarily known a-priori. The encoder E may beimplemented as a neural network (NN), recurrent neural network (RNN),GMM, or other model. One particular example is a long short-term memory(LSTM) RNN. There are a variety of ways for the encoder 750 to consumethe encoder input, including but not limited to:

-   -   linear, one direction (forward or backward),    -   bi-linear, essentially the concatenation of a forward and a        backward embedding, or    -   tree, based on parse-tree of the sequence.        In addition, an attention model can be used, which is another        RNN or deep neural network (DNN) that learns to “attract”        attention to certain parts of the input. The attention model can        be used in combination with the above methods of consuming the        input.

FIG. 7 illustrates operation of the encoder 750. The input feature valuesequence, starting with feature value x₁ 702, continuing through featurevalue x_(n) 704 and concluding with feature value x_(N) 706 is inputinto the encoder 750. The encoder 750 may process the input featurevalues as noted above. The encoder 750 outputs the encoded featurevector y 710, which is a fixed length feature vector of length F. Thus,the user recognition feature extraction component 608 may include anencoder 750 that receives audio feature values for a particularutterance as input, and outputs a fixed length encoded feature vector y710, which may be the user recognition feature/vector data 609. Thus, incertain system configurations, no matter how long the utterance is, orhow many acoustic frames worth of feature values are input into theencoder 750, the output feature vector 710/609 will be of the samelength, thus allowing for more ease of performing user recognition bythe user recognition module 802. To allow for robust system operation afinal vector 609 may include many dimensions (e.g., several hundred),thus providing many data points for downstream consideration.

To determine the user recognition feature/vector data 609, the systemmay (for example using VAD detector 610) determine that voice activityis detected in input audio. This may indicate the beginning of theutterance, thus resulting in the system determining that the inpututterance starts at a first point in audio data. Audio processing (forexample by windowing 602, FFT 604, ASR feature extraction 606, userrecognition feature extraction 608, ASR module 250, or the like) maycontinue on the utterance audio data starting at the first point andcontinuing until the VAD detector 610 determines that voice activity isno detected at a second point in audio data. Thus, the system maydetermine that the input utterance ends at the second point. Thus, thefirst point may be considered the beginpoint of the utterance and thesecond point may be considered the endpoint of the utterance. The VADdetector 610, or other component, may signal the user recognitionfeature extraction component 608 when the beginpoint and/or endpoint isdetected so that the user recognition feature extraction component 608may begin processing audio data starting at the beginpoint and ending atthe endpoint. Further, audio frames during the utterance that do notinclude speech may be filtered out by the VAD detector 610 and thus notconsidered by the ASR feature extraction 606 and/or user recognitionfeature extraction 608. The resulting accumulated/processed speech audiodata for the utterance (from beginpoint to endpoint) may then berepresented in a single feature vector for the user verificationfeature/vector data 609, which may then be used for user recognition.

FIG. 8 illustrates user verification as performed by the server(s) 120.The ASR module 250 performs ASR on the ASR feature/vector data 607 asdescribed above. ASR output (i.e., text data 300) is then processed bythe NLU module 260 as described above. The ASR confidence data 807 maythen be passed to a user recognition module 802.

The user recognition module 802 of the server(s) 120 performs userrecognition using various data including the user recognitionfeature/vector data 609 and training data 805 which may correspond tosample audio data corresponding to known users, the ASR confidence data807 and secondary data 809. The user recognition module 802 may thenoutput user recognition confidence data 811 which reflects a certainconfidence that the input utterance was spoken by one or more particularusers. The user recognition confidence data 811 may not indicate accessprivileges of the user(s). The user recognition confidence data 811 mayinclude an indicator of the verified user (such as a user IDcorresponding to the speaker of the utterance) along with a confidencevalue corresponding to the user ID, such as a numeric value or binnedvalue as discussed below.

The training data 805 may be stored in a user recognition data storage804. The user recognition data storage 804 may be stored by theserver(s) 120, or may be a separate device. Further, the userrecognition data storage 804 may be part of user profile storage 502.The user recognition data storage 804 may be a cloud-based storage. Thetraining data 805 stored in the user recognition data storage 804 may bestored as waveforms and/or corresponding features/vectors. The trainingdata 805 may correspond to data from various audio samples, each audiosample associated with a known user and/or user identity. For example,each user known to the system may be associated with some set oftraining data 805 for the known user. The user recognition module 802may then use the training data 805 to compare against incoming audiodata (represented by user recognition feature/vector data 609) todetermine the identity of a user speaking an utterance. The trainingdata 805 stored in the user recognition data storage 804 may thus beassociated with multiple users of multiple devices. Thus, the trainingdata 805 stored in the storage 804 may be associated with both a userthat spoke the respective utterance, as well as the speech-controlleddevice 110 that captured the respective utterance.

The training data 805 for a particular user may include a feature vectorof the same size as a vector of the user recognition feature/vector data609. Thus, for example, if a feature vector 609 is of size F (forexample encoded by encoder 750), the training data 805 may also be afeature vector of size F. To create such a training data feature vector,during a training period the system may either prompt a user to speaksample audio data or may identify sample audio data known to have beenspoken by a particular user. The system may then process the sampleaudio data (for example into feature values such as 702-706 and then byencoder 750) to create sample training data 805 (e.g., a feature vectorof size F). The training data 805 may then be stored by the system (suchas in data storage 804) and saved for use during runtime userverification processing.

To perform user recognition, the user recognition module 802 maydetermine the speech-controlled device 110 from which the audio data 111originated. For example, the audio data 111 may include a tag indicatingthe speech-controlled device 110. Either the speech-controlled device110 or the server(s) 120 may tag the audio data 111 as such. The tagindicating the speech-controlled device 110 may be associated with theuser recognition feature/vector data 609 produced from the audio data111. The user recognition module 802 may send a signal to the userrecognition data storage 804, with the signal requesting only trainingdata 805 associated with known users of the speech-controlled device 110from which the audio data 111 originated. This may include accessing auser profile associated with the speech-controlled device 110 and thenonly inputting training data 805 associated with users corresponding tothe user profile of the device 110. This limits the universe of possibletraining data the recognition module 802 should consider at runtime whenverifying a user and thus decreases the amount of time to perform userrecognition by decreasing the amount of training data 805 needed to beprocessed. Alternatively, the user recognition module 802 may access all(or some other subset of) training data 805 available to the system.However, accessing all training data 805 will likely increase the amountof time needed to perform user recognition based on the magnitude oftraining data to be processed.

If the user recognition module 802 receives training data 805 as anaudio waveform, the user recognition module 802 may determinefeatures/vectors of the waveform(s) or otherwise convert the waveforminto a data format that can be used by the user recognition module 802to actually perform the user recognition. The user recognition module802 may then recognize the user that spoke the utterance in the audiodata 111 by comparing features/vectors of the user recognitionfeature/vector data 609 to training features/vectors (either receivedfrom the storage 804 or determined from training data 805 received fromthe storage 804).

The user recognition module 802 may include a scoring component 822which determines respective scores indicating whether the inpututterance (represented by user recognition feature/vector data 609) wasspoken by particular users (represented by training data 805). The userrecognition module 802 may also include a confidence component 824 whichdetermines an overall confidence as the accuracy of the user recognitionoperations (such as those of the scoring component 822) and/or anindividual confidence for each user potentially identified by thescoring component 822. The output from the scoring component 822 mayinclude scores for all users with respect to which user recognition wasperformed (e.g., all users associated with the speech-controlled device110). For example, the output may include a first score for a firstuser, a second score for a second user, and third score for a thirduser, etc. Although illustrated as two separate components, the scoringcomponent 822 and confidence component 824 may be combined into a singlecomponent or may be separated into more than two components.

The scoring component 822 and confidence component 824 may implement oneor more trained machine learning models (such neural networks,classifiers, etc.) as known in the art. For example, the scoringcomponent 822 may use probabilistic linear discriminant analysis (PLDA)techniques. PLDA scoring determines how likely it is that the input userrecognition feature vector 609 corresponds to a particular training datafeature vector 805 for a particular user. The PLDA scoring may generatesimilarity scores for each training feature vector considered and mayoutput the list of scores and users and/or the user ID of the speakerwhose training data feature vector most closely corresponds to the inputuser recognition feature vector 609 of the utterance. The scoringcomponent 822 may also use other techniques such as GMMs, generativeBayesian models, or the like, to determine similarity scores.

The confidence component 824 may input various data includinginformation about the ASR confidence 807, utterance length (e.g., numberof frames or time of the utterance), audio condition/quality data (suchas signal-to-interference data or other metric data), fingerprint data,image data, or other factors to consider how confident the userrecognition module 802 is with regard to the scores linking users to theinput utterance. The confidence component 824 may also consider thesimilarity scores and user IDs output by the scoring component 822.Thus, the confidence component 824 may determine that a lower ASRconfidence 807, or poor input audio quality, or other factors, mayresult in a lower confidence of the user recognition module 802. Whereasa higher ASR confidence 807, or better input audio quality, or otherfactors, may result in a higher confidence of the user recognitionmodule 802. Precise determination of the confidence may depend onconfiguration and training of the confidence component 824 and themodels used therein. The confidence component 824 may operate using anumber of different machine learning models/techniques such as GMM,neural networks, etc. For example, the confidence component 824 may be aclassifier configured to map a score output by the scoring component 822to a confidence.

The user recognition module 802 may output user recognition confidencedata 811 specific to a single user. The user recognition confidence data811 may include a particular score (e.g., 0.0-1.0, 0-1000, or whateverscale the system is configured to operate). Alternatively or inaddition, the user recognition confidence data 811 may include a binnedrecognition indicator. For example, a computed recognition score of afirst range (e.g., 0.0-0.33) may be output as “low,” a computedrecognition score of a second range (e.g., 0.34-0.66) may be output as“medium,” and a computed recognition score of a third range (e.g.,0.67-1.0) may be output as “high.” Combined binned and confidence scoreoutputs are also possible. The user recognition module 802 may alsooutput a confidence value that the score/bin is correct, where theconfidence value indicates how confident the user recognition module 802is in the output results. This confidence value may be determined by theconfidence component 824.

The confidence component 824 may determine individual user confidencesand differences between user confidences when determining the userrecognition confidence data 811. For example, if a difference between afirst user's confidence score and a second user's confidence score islarge, and the first user's confidence score is above a threshold, thenthe user recognition module 802 is able to recognize the first user asthe user that spoke the utterance with a much higher confidence than ifthe difference between the users' confidences were smaller.

The user recognition module 802 may perform certain thresholding toavoid incorrect user recognition results being output. For example, theuser recognition module 802 may compare a confidence score output by theconfidence component 824 to a confidence threshold. If the confidencescore is not above the confidence threshold (for example, a confidenceof “medium” or higher), the user recognition module 802 may not outputuser recognition confidence data 811, or may only include in that data811 an indication that a user speaking the utterance could not beverified. Further, the user recognition module 802 may not output userrecognition confidence data 811 until enough user recognitionfeature/vector data 609 is accumulated and processed to recognize theuser above a threshold confidence. Thus the user recognition module 802may wait until a sufficient threshold quantity of audio data of theutterance has been processed before outputting user recognitionconfidence data 811. The quantity of received audio data may also beconsidered by the confidence component 824.

The user recognition module 802 may be defaulted to output binned (e.g.,low, medium, high) user recognition confidence data 811. However, suchmay be problematic from the application server(s) 125 perspective. Forexample, if the user recognition module 802 computes a single binnedconfidence for multiple users, the application server(s) 125 may not beable to determine which user to determine content with respect to. Inthis situation, the user recognition module 802 may be configured tooverride its default setting and output user recognition confidence data811 including numeric values (e.g., 0.0-1.0) associated with the usersassociated with the same binned confidence. This enables the applicationserver(s) 125 to select content associated with the user associated withthe highest confidence value. The user recognition confidence data 811may also include the user IDs corresponding to the potential user(s) whospoke the utterance.

The NLU results 385 may invoke multiple application servers 125 havingaccess to content responsive to a spoken utterance. The server(s) 120may communicate with one or more of the application servers 125 withouthaving to use a network(s) 199. Alternatively, the server(s) 120 maycommunicate with one or more of the application servers 125 through anetwork(s) 199.

Each application server 125 may have a respective user recognitionconfidence threshold that must be satisfied prior to the applicationserver 125 providing content responsive to the spoken utterance in theaudio data 111. The application server(s) 125 may individually send arequest for user recognition data to the user recognition module 802.Each request may include the user recognition confidence threshold ofthe application server 125 from which the request originated/was sent.Alternatively, a component of the server(s) 120 may compile the userrecognition confidence thresholds of all the application servers 125invoked by a spoken utterance, and may send all the user recognitionconfidence thresholds to the user recognition module 802 in a singletransmission. The user recognition confidence thresholds may be in theform of numeric confidence values (e.g., 0.0-1.0, 0-1000) or confidenceindicators (e.g., low, medium, high). All of the user recognitionconfidence thresholds sent to the user recognition module 802 may beconfidence values, all of the user recognition confidence thresholdssent to the user recognition module 802 may be in the form of confidenceindicators, or some of the user recognition confidence thresholds sentto the user recognition module 802 may be in the form of confidencevalues while others are in the form of confidence indicators.

The user recognition module 802 may determine user recognitionconfidence data 811 with respect to all of the users indicated in theprofile associated with the speech-controlled device 110. The userrecognition module 802 may then determine whether one or more users areassociated with user recognition confidence data 811 satisfying a moststringent (e.g., highest) received user recognition confidencethreshold. If the user recognition module 802 determines a single useris associated with user recognition confidence data 811 that meets orexceeds the highest user recognition confidence threshold, the userrecognition module 802 sends the user recognition confidence data 811 tothe application servers 125 from which the user recognition confidencethresholds were received.

If the user recognition module 802 does not determine a user associatedwith user recognition confidence data 811 that meets or exceeds thehighest user recognition confidence threshold, the user recognitionmodule 802 may cause speech-controlled device 110 (and/or a differentdevices indicated in profiles associated with the users indicated in thespeech-controlled device's profile) to gather additional data usable foruser recognition. Such additional data may include image data or videodata that may be used for facial recognition, for example. Suchadditional data may also include biometric data such as retina imagedata captured using a retina scanner and used for retina verification.Moreover, such additional data may include biometric data such asfingerprint data captured using a fingerprint scanner and used forfingerprint verification. Other user identifying data may also be used.The user recognition module 802 may perform user recognition usingadditional data until the user recognition module 802 determines asingle user (indicated in the profile associated with thespeech-controlled device 110) associated with user recognitionconfidence data 811 that meets or exceeds the highest user recognitionconfidence threshold. Likewise, if the user recognition module 802determines more than one user associated with user recognitionconfidence data 811 that meets or exceeds the highest user recognitionconfidence threshold, the user recognition module 802 uses additionaldata (described above) to perform user recognition until only a singleuser is associated with user recognition confidence data 811 that meetsor exceeds the highest user recognition confidence threshold.

In addition, the user recognition module 802 may use secondary data 809to inform user recognition processing. Thus, a trained model or othercomponent of the user recognition module 802 may be trained to takesecondary data 809 as an input feature when performing recognition.Secondary data 809 may include a wide variety of data types depending onsystem configuration and may be made available from other sensors,devices, or storage such as user profile data 502, etc. The secondarydata 809 may include a time of day at which the audio data was captured,a day of a week in which the audio data was captured, the text data 300,and/or the NLU results 385.

The server(s) 120 may receive the secondary data 809 from variousdevices local to the user (e.g., the speech-controlled device 110 a, asmart phone, a biometric sensor, etc.). The secondary data 809 receivedmay depend upon the situation of the user's environment. For example, ifmultiple individuals are located proximate to the devices(s) (e.g., asdetermined by multiple individuals being located within a field of viewof the camera 115 or based on multiple individuals' voices beingdetected in audio data captured by the microphone 103), the server(s)120 may receive secondary data 809 corresponding to a passcode,biometric content, or other content that may not be overheard/overseenby the other individuals. The passcode may be provided via a touchinterface of a smart device (such as a smart phone, tablet, etc.). Thebiometric content may be a fingerprint captured by a fingerprintscanner, an image(s) of a retina captured by a retina scanner, etc. Inanother example, if multiple individuals are located around the user,the secondary data 809 may only be a portion of a passcode. A userprofile may indicate an alphanumeric passcode and the server(s) 120 (oranother device) may solicit only a portion (e.g., the third digit) ofthe passcode from the user. These techniques may be beneficial if it isdetermined that multiple individuals are present because it allows theuser to provide useful verification data without having the user divulgeconfidential information to unauthorized individuals.

Each form of secondary data 809 (e.g., image data for retinaverification, passcode data, etc.) may be associated with a respectivescore/weight, or generally the determined confidence data 811 may dependon the available forms of secondary data and their respective scores, ifany. For example, a user recognition performed using retina scan datamay increase a confidence data, but the amount of increase may depend ona score associated with the retina scan data (e.g., a score from aretina scan component that produces retina scan data). Thus, it shouldbe appreciated that different forms of secondary data 809 may affectuser recognition determinations differently.

A profile associated with each user may include reference secondary data(i.e., reference retina image data, reference fingerprint image data,etc.) to which captured secondary data 809 is compared for purposes ofuser recognition. Moreover, each user profile may include reliabilityweight information associated with each type of secondary data 809. Thereliability weight information may be specific to the device configuredto capture the secondary data. For example, if the user has twodifferent retina scanners, a first retina scanner may have be associatedwith a first reliability weight and a second retina scanner may beassociated with a second reliability weight. If the user recognitionmodule 802 determines initial user recognition confidence data 811 thatdoes not satisfy the most stringent user recognition confidencethreshold, the user recognition module 802 may cause secondary data 809to be captured thereafter, and may use the secondary data 809 todetermine updated user recognition confidence data 811.

In one example, secondary data 809 may include image data or video data.For example, facial recognition may be performed on image data or videodata received from the speech-controlled device 110 from which the audiodata 111 was received. Facial recognition may be performed by the userrecognition module 802, or another component of the server(s) 120. Theoutput of the facial recognition process may be used by the userrecognition module 802. That is, facial recognition output data may beused in conjunction with the comparison of the features/vectors of theaudio data 111 and training data 805 to perform more accurate userrecognition. Thus, while training data 805 may be based on speechsamples of a user, the training data 805 may also be based on other datasuch as image data corresponding to known pictures of the user, retinascan data corresponding to the user, fingerprint data, etc.

The secondary data 809 may also include location data of thespeech-controlled device 110. The location data may be specific to abuilding within which the speech-controlled device 110 is located. Forexample, if the speech-controlled device 110 is located in user A′sbedroom, such location may increase user recognition confidence dataassociated with user A, but decrease user recognition confidence dataassociated with user B.

The secondary data 809 may further include type data indicating a typeof the speech-controlled device 110. Different types ofspeech-controlled devices may include, for example, a smart watch, asmart phone, a tablet computer, and a vehicle. The type of thespeech-controlled device 110 may be indicated in a profile associatedwith the speech-controlled device 110. For example, if thespeech-controlled device 110 from which the audio data 111 was receivedis a smart watch or vehicle belonging to user A, the fact that thespeech-controlled device 110 belongs to user A may increase userrecognition confidence data associated with user A, but decrease userrecognition confidence data associated with user B.

The secondary data 809 may additionally include geographic coordinatedata associated with the speech-controlled device 110. For example, aprofile associated with a vehicle may indicate multiple users (e.g.,user A and user B). The vehicle may include a global positioning system(GPS) indicating latitude and longitude coordinates of the vehicle whenthe audio data 111 is captured by the vehicle. As such, if the vehicleis located at a coordinate corresponding to a work location/building ofuser A, such may increase user recognition confidence data associatedwith user A, but decrease user recognition confidence data of all otherusers indicated in the profile associated with the vehicle. Globalcoordinates and associated locations (e.g., work, home, etc.) may beindicated in a user profile associated with the speech-controlled device110. The global coordinates and associated locations may be associatedwith respective users in the user profile.

The secondary data 809 may also include other data/signals aboutactivity of a particular user that may be useful in performing userrecognition of an input utterance. For example, if a user has recentlyentered a code to disable a home security alarm, and the utterancecorresponds to a device at the home, signals from the home securityalarm about the disabling user, time of disabling, etc. may be reflectedin the secondary data 809 and considered by the user recognition module802. If a mobile device (such as a phone, Tile, dongle, or other device)known to be associated with a particular user is detected proximate to(for example physically close to, connected to the same WiFi network as,or otherwise nearby) the speech-controlled device 110, this may bereflected in the secondary data 809 and considered by the userrecognition module 802.

Depending on system configuration, the secondary data 809 may beconfigured to be included in the vector representation of the userrecognition feature/vector data 609 (for example using encoder 750) sothat all the data relating to the utterance to be processed by thescoring component 822 may be included in a single vector. Alternatively,the secondary data 809 may be reflected in one or more different datastructures to be processed by the scoring component 822.

As shown in FIG. 8, the ASR module 250 may output text data 300, whichin turn is processed by the NLU component 260. The results 385 of NLUprocessing performed by the NLU module 260 and the user recognitionconfidence data 811 output by the user recognition module 802 may besent to one or more applications, represented by application server(s)125. The NLU results 385 and the user recognition confidence data 811may be sent simultaneously (i.e., in a single transmission) to theapplication server(s) 125 via an application program interface (API).Alternatively, the NLU results 385 and the user recognition confidencedata 811 may be sent in consecutive transmissions to the applicationserver(s) 125 via the API. The application server(s) 125 that receivesthe NLU results 385 and the user recognition confidence score data 811may be determined by the server(s) 120 as corresponding to contentresponsive to the utterance in the audio data 111. For example, if theaudio data 111 includes the utterance “Play my music,” the NLU results385 and user recognition confidence data 811 may be sent to a musicplaying application server 125. If user recognition confidence data 811associated with a single user is passed to the application server(s)125, the application server(s) 125 may identify content responsive tothe utterance and associated with the user. If, instead, userrecognition confidence score data 811 associated with multiple users ispassed to the application server(s) 125, the application server(s) 125may identify content responsive to the utterance and associated with theuser associated with the highest recognition confidence.

The API used to pass the user recognition confidence data 811 to theapplication server(s) 125 may allow the application server(s) 125 toreceive various information such a user ID as well as a confidence valuecorresponding to the user ID, such as a numeric value or binned value asdiscussed herein. The API may also pass data corresponding to orrepresenting the confidence value such as a binned value discussedherein or other data, numeric or otherwise representing the confidence.The API may also pass other data such as a source of user recognitiondata (e.g., whether the system recognized the user using speechanalysis, a passcode, a passphrase, a fingerprint, biometric data, etc.or some combination thereof). The API may also pass data such as otheruser profile information, a speech session identifier (so the variouscomponents can track the speech session corresponding to the data) orother information. The speech session identifier may correspond to anutterance spoken by a user and/or to an ongoing exchange between thesystem and the user (such as a dialog of multiple utterances) toexchange information for executing a command. The API, or one or moreother APIs, may also be used to exchange the user recognition confidencethresholds sent from the application server(s) 125 to the userrecognition module 802 as well as other data such as requested specificsources of user recognition data, user IDs, speech session IDs,requested content data, or other information to be exchanged forpurposes of processing a speech command/session.

Various machine learning techniques may be used to train and operatemodels to perform various steps described above, such as userrecognition feature extraction, encoding, user recognition scoring, userrecognition confidence determination, etc. Models may be trained andoperated according to various machine learning techniques. Suchtechniques may include, for example, neural networks (such as deepneural networks and/or recurrent neural networks), inference engines,trained classifiers, etc. Examples of trained classifiers includeSupport Vector Machines (SVMs), neural networks, decision trees,AdaBoost (short for “Adaptive Boosting”) combined with decision trees,and random forests. Focusing on SVM as an example, SVM is a supervisedlearning model with associated learning algorithms that analyze data andrecognize patterns in the data, and which are commonly used forclassification and regression analysis. Given a set of trainingexamples, each marked as belonging to one of two categories, an SVMtraining algorithm builds a model that assigns new examples into onecategory or the other, making it a non-probabilistic binary linearclassifier. More complex SVM models may be built with the training setidentifying more than two categories, with the SVM determining whichcategory is most similar to input data. An SVM model may be mapped sothat the examples of the separate categories are divided by clear gaps.New examples are then mapped into that same space and predicted tobelong to a category based on which side of the gaps they fall on.Classifiers may issue a “score” indicating which category the data mostclosely matches. The score may provide an indication of how closely thedata matches the category.

In order to apply the machine learning techniques, the machine learningprocesses themselves need to be trained. Training a machine learningcomponent such as, in this case, one of the first or second models,requires establishing a “ground truth” for the training examples. Inmachine learning, the term “ground truth” refers to the accuracy of atraining set's classification for supervised learning techniques.Various techniques may be used to train the models includingbackpropagation, statistical learning, supervised learning,semi-supervised learning, stochastic learning, or other knowntechniques.

Different content sources may require different user recognitionconfidence level thresholds be satisfied prior to creating/releasinguser specific content. For example, if a user says “What is my bankaccount balance”, an output of the system may ultimately be “Hello John,your Bank A account balance is $500.” The portion of the outputcorresponding to “Hello John” may be created by a TTS content sourcethat requires a user recognition confidence threshold of “low” besatisfied since potentially speaking the wrong user's name in a greetingis not necessarily problematic from a confidentiality perspective. Incontrast, the portion of the output corresponding to “your Bank Aaccount balance is $500” may be created using output from a bankingcontent source (e.g., an application server 125) that requires a userrecognition confidence threshold of “high” be satisfied becausedivulging a bank and an account balance of a user to a wrong user ishighly problematic from a confidentiality perspective.

Moreover, a single content source may require different user recognitionconfidence level thresholds be satisfied based on the type/kind ofcontent to be released. For example, a banking application may beconfigured to create/release bank branch information (e.g., branchlocations and times of operation). The banking application may also beconfigured to release bank account information. The banking applicationmay require a user recognition confidence level of “low” be satisfied torelease bank branch information, but may require a user recognitionconfidence level of “high” be satisfied to release user specific bankaccount information.

FIGS. 9A through 9C illustrate user recognition involving a feedbackloop for gathering addition user recognition data where needed, andwherein user recognition confidence thresholds are received from contentsources. The speech-controlled device 110 captures (902) input audioincluding a spoken utterance. The speech-controlled device 110 sends(904) input audio data, corresponding to the input audio, to theserver(s) 120.

The server(s) 120 may perform (906) ASR on the input audio data todetermine input text data. The server(s) 120 may also perform (908) NLUon the input text data. Using output of the NLU processing, theserver(s) 120 (namely a component of the server(s) 120 such as thecommand processor(s) 290) may determine (910) one or multiple contentsources storing or having access to content responsive to the spokenutterance. For example, the NLU output may relate to multiple domains(e.g., music, video, books, etc.), and each content source may beassociated with a respective domain. The server(s) 120 may send (912) asignal to each determined content source (e.g., command processor290/application server 125 associated with the domain/command to beexecuted), with the signal requesting respective content from thecontent source. The command processor 290/content source may be locatedwithin the system or may be associated with an application server 125(not illustrated).

The server(s) 120 (e.g., the user verification module 802) may receive(914) a respective user recognition confidence threshold from eachcontent source. Alternatively, the server(s) 120 may determine userrecognition confidence threshold data associated with each contentsource within a lookup table. This user recognition confidence thresholdmay relate to the command to be performed and/or to the content data tobe obtained. In certain examples, a single content source may returnmultiple different user recognition confidence thresholds, where eachthreshold corresponds to certain content data that may result from theutterance. The server(s) 120 may, using the user recognition data,determine (916 illustrated in FIG. 9B) a highest (i.e., most stringent)user recognition confidence threshold of those received from the contentsources. The server(s) 120 may determine (918) user recognition data fora single user indicated in a profile associated with thespeech-controlled device 110 (i.e., the user associated with the highestrecognition confidence determined by the user recognition module 802).The server(s) 120 may also determine (920) whether the highest userrecognition confidence threshold is satisfied (i.e., whether the systemhas sufficiently verified the user associated with the input utteranceso that the user recognition confidence meets or exceeds the highestuser recognition confidence threshold from the group of contentsources). If the user recognition data does not meet or exceed thehighest user recognition confidence threshold, the server(s) 120 maysend (922) a signal to the speech-controlled device 110 (or anotherdevice associated with the speech-controlled device 110, such as otherrecognition devices such as biometric sensors) to obtain additional userrecognition data. The additional user recognition data may include, forexample, image data or video data usable to perform facial recognition,fingerprint data usable to perform fingerprint verification, retinaimage data usable to perform retina verification, etc. Thespeech-controlled device 110 (and/or another device) captures (924)additional user recognition data and sends (926) the additional userrecognition data to the server(s) 120. The server(s) 120 then repeatssteps 9918 and 920. For example, the server(s) 120 may determine abiometric device (or other device) associated with the profile of thespeech-controlled device 110. The server(s) 120 may then cause therecognition device (which may be device 110 or a different device) tocapture and/or send recognition data. The server(s) 120 may receive therecognition data, and may compare it to stored recognition dataassociated with users indicated in the profile of the speech-controlleddevice 110. Based on the comparison, the server(s) 120 may update theuser recognition data.

If the server(s) 120 (either originally or after gathering additionaluser recognition data) determines the user recognition data meets orexceeds the highest user recognition confidence threshold, the server(s)120 sends (928 illustrated in FIG. 9C) a signal indicating the user andassociated recognition data to all of the content sources. The signalmay include specific recognition confidence data (such as a confidencescore or a confidence indicator) associated with the user.Alternatively, the signal may simply indicate the user associated withuser recognition data that meets or exceeds the highest threshold.

The server(s) 120 may thereafter receive (930) content from therespective content sources, and send (932) the content to thespeech-controlled device 110. The content received from the contentsources may be in the form of text data. The speech-controlled device110 may then output (934) content corresponding to the text data. Forexample, the server(S) 120 may perform TTS on the received text data tocreate output audio data, and the output audio data may be sent to andoutput from the speech-controlled device 110.

For example, the server(s) 120 may perform NLU on input audio data, andmay determine a TTS prompter and a bank application should be used togenerate output audio responsive to the input audio data. The server(s)120 may also determine that the TTS prompter requires a low userrecognition confidence threshold be satisfied, and that the bankapplication requires a high user recognition confidence threshold besatisfied. The server(s) 120 may then perform audio based userrecognition using the input audio data, and determine a high userrecognition confidence. Thereafter, the server(s) 120 may indicate, toboth the TTS prompter and the bank application, that their thresholdshave been satisfied (with or without indicating the specifics of thegenerated confidence, that is a high confidence). The TTS prompter andbank application may then provide respective data, and the server(s) 120may output responsive content to the user.

For further example, the server(s) 120 may perform NLU on input audiodata, and may determine a TTS prompter and a bank application should beused to generate output audio responsive to the input audio data. Theserver(s) 120 may also determine that the TTS prompter requires a lowuser recognition confidence threshold be satisfied, and that the bankapplication requires a high user recognition confidence threshold besatisfied. The server(s) 120 may then perform audio based userrecognition using the input audio data, and determine a medium userrecognition confidence. Thus, rather than allow only a portion of aresponsive output be generated (since the medium confidence issufficient for the TTS prompter but insufficient for the bankapplication), the server(s) 120 may cause the speech-controlled device110 (or another device) to prompt the user for additional recognitiondata. Such a prompt may include, for example, “Please provide anadditional verification input so that I may more accurately verify you.”The server(s) 120 may use the additional recognition data in conjunctionwith the previous audio recognition to determine a user confidence ofhigh. The server(s) 120 may then indicate, to both the TTS prompter andthe bank application, that their thresholds have been satisfied (with orwithout indicating the specifics of the generated confidence, that is ahigh confidence). The TTS prompter and bank application may then providerespective data, and the server(s) 120 may output responsive content tothe user.

FIG. 10 is a block diagram conceptually illustrating a user device 110(e.g., the speech-controlled device 110 described herein) that may beused with the described system. FIG. 11 is a block diagram conceptuallyillustrating example components of a remote device, such as the server120 that may assist with ASR processing, NLU processing, or commandprocessing. Multiple servers 120 may be included in the system 100, suchas one server 120 for performing ASR, one server 120 for performing NLU,etc. In operation, each of these devices (or groups of devices) mayinclude computer-readable and computer-executable instructions thatreside on the respective device (110/120), as will be discussed furtherbelow.

Each of these devices (110/120) may include one or morecontrollers/processors (1004/1104), that may each include a centralprocessing unit (CPU) for processing data and computer-readableinstructions, and a memory (1006/1106) for storing data and instructionsof the respective device. The memories (1006/1106) may individuallyinclude volatile random access memory (RAM), non-volatile read onlymemory (ROM), non-volatile magnetoresistive (MRAM) and/or other types ofmemory. Each device (110/120) may also include a data storage component(1008/1108), for storing data and controller/processor-executableinstructions. Each data storage component may individually include oneor more non-volatile storage types such as magnetic storage, opticalstorage, solid-state storage, etc. Each device (110/120) may also beconnected to removable or external non-volatile memory and/or storage(such as a removable memory card, memory key drive, networked storage,etc.) through respective input/output device interfaces (1002/1102).

Computer instructions for operating each device (110/120) and itsvarious components may be executed by the respective device'scontroller(s)/processor(s) (1004/1104), using the memory (1006/1106) astemporary “working” storage at runtime. A device's computer instructionsmay be stored in a non-transitory manner in non-volatile memory(1006/1106), storage (1008/1108), or an external device(s).Alternatively, some or all of the executable instructions may beembedded in hardware or firmware on the respective device in addition toor instead of software.

Each device (110/120) includes input/output device interfaces(1002/1102). A variety of components may be connected through theinput/output device interfaces (1002/1102), as will be discussed furtherbelow. Additionally, each device (110/120) may include an address/databus (1024/1124) for conveying data among components of the respectivedevice. Each component within a device (110/120) may also be directlyconnected to other components in addition to (or instead of) beingconnected to other components across the bus (1024/1124).

Referring to FIG. 10, the device 110 may include input/output deviceinterfaces 1002 that connect to a variety of components such as an audiooutput component such as a speaker 101, a wired headset or a wirelessheadset (not illustrated), or other component capable of outputtingaudio. The device 110 may also include an audio capture component. Theaudio capture component may be, for example, a microphone 103 or arrayof microphones, a wired headset or a wireless headset (not illustrated),etc. The microphone 103 may be configured to capture audio. If an arrayof microphones is included, approximate distance to a sound's point oforigin may be determined by acoustic localization based on time andamplitude differences between sounds captured by different microphonesof the array. The device 110 may additionally include an image or videocapture component, such as the camera 115. The camera 115 may beconfigured to capture data used to perform facial recognition, andultimately user recognition.

For example, via antenna(s) 1014, the input/output device interfaces1002 may connect to one or more networks 199 via a wireless local areanetwork (WLAN) (such as WiFi) radio, Bluetooth, and/or wireless networkradio, such as a radio capable of communication with a wirelesscommunication network such as a Long Term Evolution (LTE) network, WiMAXnetwork, 3G network, etc. A wired connection such as Ethernet may alsobe supported. Through the network(s) 199, the speech processing systemmay be distributed across a networked environment.

Referring to FIG. 11, the server 120 may include a user recognitionmodule 802 configured to perform user recognition as described herein.The server 120 may also be configured with the TTS module 414, AFE 256,or other components as described herein.

The device 110 and/or the server 120 may include an ASR module 250. TheASR module 250 in the device 110 may be of limited or extendedcapabilities. The ASR module 250 may include the language models 254stored in ASR model storage component 252. If limited speech recognitionis included, the ASR module 250 may be configured to identify a limitednumber of words, whereas extended speech recognition may be configuredto recognize a much larger range of words.

The device 110 and/or the server 120 may include a limited or extendedNLU module 260. The NLU module 260 in the device 110 may be of limitedor extended capabilities. The NLU module 260 may comprise the nameentity recognition module 262, the intent classification module 264,and/or other components. The NLU module 260 may also include a storedknowledge base and/or entity library, or those storages may beseparately located.

The device 110 and/or the server 120 may also include the commandprocessor 290 configured to execute commands/functions associated with aspoken utterance as described herein.

As noted above, multiple devices may be employed in a single speechprocessing system. In such a multi-device system, each of the devicesmay include different components for performing different aspects of thespeech processing. The multiple devices may include overlappingcomponents. The components of the device 110 and the server 120, asillustrated in FIGS. 10 and 11, are exemplary, and may be located as astand-alone device or may be included, in whole or in part, as acomponent of a larger device or system.

As illustrated in FIG. 12, multiple devices (120, 110 a-110 e, 1202,1204) may contain components of the system 100 and the devices may beconnected over a network 199. The network 199 may include a local orprivate network or may include a wide network such as the Internet.Devices may be connected to the network 199 through either wired orwireless connections. For example, the speech-controlled device 110 a, asmart phone 110 b, a smart watch 110 c, a tablet computer 110 d, and/ora vehicle 110 e may be connected to the network 199 through a wirelessservice provider, over a WiFi or cellular network connection, or thelike. Other devices are included as network-connected support devices,such as the server 120 or others. Other devices are included asnetwork-connected biometric sensors, such as a fingerprint scanner 1102,a retina scanner 1104, and the like. The support devices may connect tothe network 199 through a wired connection or wireless connection.Networked devices may capture audio using one-or-more built-in orconnected microphones 103 or audio capture devices, with processingperformed by ASR, NLU, or other components of the same device or anotherdevice connected via the network 199, such as the ASR module 250, theNLU module 260, etc. of one or more servers 120.

The concepts disclosed herein may be applied within a number ofdifferent devices and computer systems, including, for example,general-purpose computing systems, speech processing systems, objectrecognition (e.g., facial recognition) systems, and distributedcomputing environments.

The above aspects of the present disclosure are meant to beillustrative. They were chosen to explain the principles and applicationof the disclosure and are not intended to be exhaustive or to limit thedisclosure. Many modifications and variations of the disclosed aspectsmay be apparent to those of skill in the art. Persons having ordinaryskill in the field of computers, speech processing, object recognition(e.g., facial recognition), and user recognition should recognize thatcomponents and process steps described herein may be interchangeablewith other components or steps, or combinations of components or steps,and still achieve the benefits and advantages of the present disclosure.Moreover, it should be apparent to one skilled in the art that thedisclosure may be practiced without some or all of the specific detailsand steps disclosed herein.

Aspects of the disclosed system may be implemented as a computer methodor as an article of manufacture such as a memory device ornon-transitory computer readable storage medium. The computer readablestorage medium may be readable by a computer and may compriseinstructions for causing a computer or other device to perform processesdescribed in the present disclosure. The computer readable storagemedium may be implemented by a volatile computer memory, non-volatilecomputer memory, hard drive, solid-state memory, flash drive, removabledisk, and/or other media. In addition, components of one or more of themodules and engines may be implemented as in firmware or hardware, suchas the AFE 256, which comprises, among other things, analog and/ordigital filters (e.g., filters configured as firmware to a digitalsignal processor (DSP)).

As used in this disclosure, the term “a” or “one” may include one ormore items unless specifically stated otherwise. Further, the phrase“based on” is intended to mean “based at least in part on” unlessspecifically stated otherwise.

What is claimed is:
 1. A computer-implemented method comprising:receiving, from a first device, input audio data representing firstspeech; determining first stored data associated with the first device,the first stored data corresponding to a voice of a first user;processing the input audio data with respect to the first stored data todetermine the first speech was spoken by the first user; performingspeech processing using the input audio data to determine a request;determining the request corresponds to a first component configured toperform an action in response to the request; sending, to the firstcomponent, an indication corresponding to the request; and sending, tothe first component, an indication corresponding to the first user. 2.The computer-implemented method of claim 1, further comprising:determining the request corresponds to playback of content; determiningthe first component corresponds to the content; and causing the contentto be output, wherein the content is personalized for the first user. 3.The computer-implemented method of claim 1, further comprising:determining second stored data associated with the first device, thesecond stored data corresponding to a voice of a second user; andprocessing the input audio data with respect to the second stored datato determine the first speech was not spoken by the second user.
 4. Thecomputer-implemented method of claim 1, further comprising: receiving,from the first device, image data; processing the image data todetermine a representation of a face of the first user; and causingcontent corresponding to the first user to be displayed using the firstdevice.
 5. The computer-implemented method of claim 1, furthercomprising, prior to receiving the input audio data: receiving secondaudio data corresponding to the first user; processing the second audiodata to determine the first stored data; and associating the firststored data with the first device.
 6. The computer-implemented method ofclaim 1, further comprising: determining the first device is associatedwith a profile; and identifying the first stored data using the profile.7. The computer-implemented method of claim 1, wherein: the indicationcorresponding to the request is sent using an application programminginterface (API); and the indication corresponding to the first user issent using the API.
 8. The computer-implemented method of claim 1,further comprising: determining a user recognition conditioncorresponding to the request; and based at least in part on theprocessing of the input audio data with respect to the first storeddata, determining that the user recognition condition is satisfied. 9.The computer-implemented method of claim 1, further comprising:determining a speech session identifier corresponding to the input audiodata; and sending, to the first component, the speech sessionidentifier.
 10. The computer-implemented method of claim 1, whereinprocessing the input audio data with respect to the first stored datacomprises: processing the input audio data to determine feature data;and processing the feature data with respect to the first stored data.11. A system comprising: at least one processor; and at least one memorycomprising instructions that, when executed by the at least oneprocessor, cause the system to: receive, from a first device, inputaudio data representing first speech; determine first stored dataassociated with the first device, the first stored data corresponding toa voice of a first user; process the input audio data with respect tothe first stored data to determine the first speech was spoken by thefirst user; perform speech processing using the input audio data todetermine a request; determine the request corresponds to a firstcomponent configured to perform an action in response to the request;send, to the first component, an indication corresponding to therequest; and send, to the first component, an indication correspondingto the first user.
 12. The system of claim 11, wherein the at least onememory further comprises instructions that, when executed by the atleast one processor, further cause the system to: determine the requestcorresponds to playback of content; determine the first componentcorresponds to the content; and cause the content to be output, whereinthe content is personalized for the first user.
 13. The system of claim11, wherein the at least one memory further comprises instructions that,when executed by the at least one processor, further cause the systemto: determine second stored data associated with the first device, thesecond stored data corresponding to a voice of a second user; andprocess the input audio data with respect to the second stored data todetermine the first speech was not spoken by the second user.
 14. Thesystem of claim 11, wherein the at least one memory further comprisesinstructions that, when executed by the at least one processor, furthercause the system to: receive, from the first device, image data; processthe image data to determine a representation of a face of the firstuser; and cause content corresponding to the first user to be displayedusing the first device.
 15. The system of claim 11, wherein the at leastone memory further comprises instructions that, when executed by the atleast one processor, further cause the system to: prior to receiving theinput audio data: receive second audio data corresponding to the firstuser; process the second audio data to determine the first stored data;and associate the first stored data with the first device.
 16. Thesystem of claim 11, wherein the at least one memory further comprisesinstructions that, when executed by the at least one processor, furthercause the system to: determine the first device is associated with aprofile; and identify the first stored data using the profile.
 17. Thesystem of claim 11, wherein: the indication corresponding to the requestis sent using an application programming interface (API); and theindication corresponding to the first user is sent using the API. 18.The system of claim 11, wherein the at least one memory furthercomprises instructions that, when executed by the at least oneprocessor, further cause the system to: determine a user recognitioncondition corresponding to the request; and based at least in part onprocessing of the input audio data with respect to the first storeddata, determine that the user recognition condition is satisfied. 19.The system of claim 11, wherein the at least one memory furthercomprises instructions that, when executed by the at least oneprocessor, further cause the system to: determine a speech sessionidentifier corresponding to the input audio data; and send, to the firstcomponent, the speech session identifier.
 20. The system of claim 11,wherein the instructions that cause the system to process the inputaudio data with respect to the first stored data comprise instructionsthat, when executed by the at least one processor, cause the system to:process the input audio data to determine feature data; and process thefeature data with respect to the first stored data.